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Given the following machine learning model name: Mix-FFN, provide a description of the model | **Mix-FFN** is a feedforward layer used in the [SegFormer](https://paperswithcode.com/method/segformer) architecture. [ViT](https://www.paperswithcode.com/method/vision-transformer) uses [positional encoding](https://paperswithcode.com/methods/category/position-embeddings) (PE) to introduce the location information. Ho... |
Given the following machine learning model name: Multi-DConv-Head Attention, provide a description of the model | **Multi-DConv-Head Attention**, or **MDHA**, is a type of [Multi-Head Attention](https://paperswithcode.com/method/multi-head-attention) that utilizes [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution) after the multi-head projections. It is used in the [Primer](https://paperswithcode.com... |
Given the following machine learning model name: Lifelong Infinite Mixture, provide a description of the model | **LIMix**, or **Lifelong Infinite Mixture**, is a lifelong learning model which grows a mixture of models to adapt to an increasing number of tasks. LIMix can automatically expand its network architectures or choose an appropriate component to adapt its parameters for learning a new task, while preserving its previous... |
Given the following machine learning model name: Crossbow, provide a description of the model | **Crossbow** is a single-server multi-GPU system for training deep learning models that enables users to freely choose their preferred batch size—however small—while scaling to multiple GPUs. Crossbow uses many parallel model replicas and avoids reduced statistical efficiency through a new synchronous training method. ... |
Given the following machine learning model name: Alternating Direction Method of Multipliers, provide a description of the model | The **alternating direction method of multipliers** (**ADMM**) is an algorithm that solves convex optimization problems by breaking them into smaller pieces, each of which are then easier to handle. It takes the form of a decomposition-coordination procedure, in which the solutions to small
local subproblems are coord... |
Given the following machine learning model name: Sarsa Lambda, provide a description of the model | **Sarsa_INLINE_MATH_1** extends eligibility-traces to action-value methods. It has the same update rule as for **TD_INLINE_MATH_1** but we use the action-value form of the TD erorr:
$$ \delta\_{t} = R\_{t+1} + \gamma\hat{q}\left(S\_{t+1}, A\_{t+1}, \mathbb{w}\_{t}\right) - \hat{q}\left(S\_{t}, A\_{t}, \mathbb{w}\_{t... |
Given the following machine learning model name: PReLU-Net, provide a description of the model | **PReLU-Net** is a type of convolutional neural network that utilises parameterized ReLUs for its activation function. It also uses a robust initialization scheme - afterwards known as [Kaiming Initialization](https://paperswithcode.com/method/he-initialization) - that accounts for non-linear activation functions. |
Given the following machine learning model name: KungFu, provide a description of the model | **KungFu** is a distributed ML library for TensorFlow that is designed to enable adaptive training. KungFu allows users to express high-level Adaptation Policies (APs) that describe how to change hyper- and system parameters during training. APs take real-time monitored metrics (e.g. signal-to-noise ratios and noise sc... |
Given the following machine learning model name: Lower Bound on Transmission using Non-Linear Bounding Function in Single Image Dehazing, provide a description of the model | |
Given the following machine learning model name: PermuteFormer, provide a description of the model | **PermuteFormer** is a [Performer](https://paperswithcode.com/method/performer)-based model with relative position encoding that scales linearly on long sequences. PermuteFormer applies position-dependent transformation on queries and keys to encode positional information into the attention module. This transformation ... |
Given the following machine learning model name: Hyperboloid Embeddings, provide a description of the model | Hyperboloid Embeddings (HypE) is a novel self-supervised dynamic reasoning framework, that utilizes positive first-order existential queries on a KG to learn representations of its entities and relations as hyperboloids in a Poincaré ball. HypE models the positive first-order queries as geometrical translation (t), int... |
Given the following machine learning model name: Conditional Instance Normalization, provide a description of the model | **Conditional Instance Normalization** is a normalization technique where all convolutional weights of a style transfer network are shared across many styles. The goal of the procedure is transform
a layer’s activations $x$ into a normalized activation $z$ specific to painting style $s$. Building off
[instance norma... |
Given the following machine learning model name: Single-Headed Attention, provide a description of the model | **Single-Headed Attention** is a single-headed attention module used in the [SHA-RNN](https://paperswithcode.com/method/sha-rnn) language model. The principle design reasons for single-headedness were simplicity (avoiding running out of memory) and scepticism about the benefits of using multiple heads. |
Given the following machine learning model name: Deep Orthogonal Fusion of Local and Global Features, provide a description of the model | Image Retrieval is a fundamental task of obtaining images similar to the query one from a database. A common image retrieval practice is to firstly retrieve candidate images via similarity search using global image features and then re-rank the candidates by leveraging their
local features. Previous learning-based stu... |
Given the following machine learning model name: VarifocalNet, provide a description of the model | **VarifocalNet** is a method aimed at accurately ranking a huge number of candidate detections in object detection. It consists of a new loss function, named [Varifocal Loss](https://paperswithcode.com/method/varifocal-loss), for training a dense object detector to predict the IACS, and a new efficient star-shaped boun... |
Given the following machine learning model name: IICNet, provide a description of the model | **Invertible Image Conversion Net**, or **IICNet**, is a generic framework for reversible image conversion tasks. Unlike previous encoder-decoder based methods, IICNet maintains a highly invertible structure based on invertible neural networks (INNs) to better preserve the information during conversion. It uses a relat... |
Given the following machine learning model name: Cross-View Training, provide a description of the model | **Cross View Training**, or **CVT**, is a semi-supervised algorithm for training distributed word representations that makes use of unlabelled and labelled examples.
CVT adds $k$ auxiliary prediction modules to the model, a Bi-[LSTM](https://paperswithcode.com/method/lstm) encoder, which are used when learning on u... |
Given the following machine learning model name: SGD with Momentum, provide a description of the model | ### Why SGD with Momentum?
In deep learning, we have used stochastic gradient descent as one of the optimizers because at the end we will find the minimum weight and bias at which the model loss is lowest. In the SGD we have some issues in which the SGD does not work perfectly because in deep learning we got a non-co... |
Given the following machine learning model name: Fast R-CNN, provide a description of the model | **Fast R-CNN** is an object detection model that improves in its predecessor [R-CNN](https://paperswithcode.com/method/r-cnn) in a number of ways. Instead of extracting CNN features independently for each region of interest, Fast R-CNN aggregates them into a single forward pass over the image; i.e. regions of interest ... |
Given the following machine learning model name: RepVGG, provide a description of the model | **RepVGG** is a [VGG](https://paperswithcode.com/method/vgg)-style convolutional architecture. It has the following advantages:
- The model has a VGG-like plain (a.k.a. feed-forward) topology 1 without any branches. I.e., every layer takes
the output of its only preceding layer as input and feeds the output into it... |
Given the following machine learning model name: Stochastic Dueling Network, provide a description of the model | A **Stochastic Dueling Network**, or **SDN**, is an architecture for learning a value function $V$. The SDN learns both $V$ and $Q$ off-policy while maintaining consistency between the two estimates. At each time step it outputs a stochastic estimate of $Q$ and a deterministic estimate of $V$. |
Given the following machine learning model name: Feature Fusion Module v1, provide a description of the model | **Feature Fusion Module v1** is a feature fusion module from the [M2Det](https://paperswithcode.com/method/m2det) object detection model, and feature fusion modules are crucial for constructing the final multi-level feature pyramid. They use [1x1 convolution](https://paperswithcode.com/method/1x1-convolution) layers to... |
Given the following machine learning model name: PyTorch DDP, provide a description of the model | **PyTorch DDP** (Distributed Data Parallel) is a distributed data parallel implementation for PyTorch. To guarantee mathematical equivalence, all replicas start from the same initial values for model parameters and synchronize gradients to keep parameters consistent across training iterations. To minimize the intrusive... |
Given the following machine learning model name: ConvLSTM, provide a description of the model | **ConvLSTM** is a type of recurrent neural network for spatio-temporal prediction that has convolutional structures in both the input-to-state and state-to-state transitions. The ConvLSTM determines the future state of a certain cell in the grid by the inputs and past states of its local neighbors. This can easily be a... |
Given the following machine learning model name: Adabelief, provide a description of the model | |
Given the following machine learning model name: Label Smoothing, provide a description of the model | **Label Smoothing** is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the likelihood of $\log{p}\left(y\mid{x}\right)$ directly can be harmful. Assume for a small constant $\epsilon$, the training set label $y$ is correc... |
Given the following machine learning model name: Temporaral Difference Network, provide a description of the model | **TDN**, or **Temporaral Difference Network**, is an action recognition model that aims to capture multi-scale temporal information. To fully capture temporal information over the entire video, the TDN is established with a two-level difference modeling paradigm. Specifically, for local motion modeling, temporal differ... |
Given the following machine learning model name: Global second-order pooling convolutional networks, provide a description of the model | A Gsop block has a squeeze module and an excitation module, and uses a second-order pooling to model high-order statistics while gathering global information.
In the squeeze module, a GSoP block firstly reduces the number of channels from $c$ to $c'$ ($c' < c$) using a $1 \times 1$ convolution, then computes a $c' \... |
Given the following machine learning model name: Domain Adaptive Ensemble Learning, provide a description of the model | **Domain Adaptive Ensemble Learning**, or **DAEL**, is an architecture for domain adaptation. The model is composed of a CNN feature extractor shared across domains and multiple classifier heads each trained to specialize in a particular source domain. Each such classifier is an expert to its own domain and a non-exper... |
Given the following machine learning model name: uNetXST, provide a description of the model | uNet neural network architecture which takes multiple (X) tensors as input and contains [Spatial Transformer](https://paperswithcode.com/method/spatial-transformer) units (ST) |
Given the following machine learning model name: Prime Dilated Convolution, provide a description of the model | |
Given the following machine learning model name: Adaptive Hybrid Activation Function, provide a description of the model | Trainable activation function as a sigmoid-based generalization of ReLU, Swish and SiLU. |
Given the following machine learning model name: CP with N3 Regularizer and Relation Prediction, provide a description of the model | CP with N3 Regularizer and Relation Prediction |
Given the following machine learning model name: In-Place Activated Batch Normalization, provide a description of the model | **In-Place Activated Batch Normalization**, or **InPlace-ABN**, substitutes the conventionally used succession of [BatchNorm](https://paperswithcode.com/method/batch-normalization) + Activation layers with a single plugin layer, hence avoiding invasive framework surgery while providing straightforward applicability for... |
Given the following machine learning model name: MetaFormer, provide a description of the model | MetaFormer is a general architecture abstracted from Transformers by not specifying the token mixer. |
Given the following machine learning model name: Adaptive NMS, provide a description of the model | **Adaptive Non-Maximum Suppression** is a non-maximum suppression algorithm that applies a dynamic suppression threshold to an instance according to the target density. The motivation is to find an NMS algorithm that works well for pedestrian detection in a crowd. Intuitively, a high NMS threshold keeps more crowded in... |
Given the following machine learning model name: FRILL, provide a description of the model | **FRILL** is a non-semantic speech embedding model trained via knowledge distillation that is fast enough to be run in real-time on a mobile device. The fastest model runs at 0.9 ms, which is 300x faster than TRILL and 25x faster than TRILL-distilled. |
Given the following machine learning model name: InterBERT, provide a description of the model | InterBERT aims to model interaction between information flows pertaining to different modalities. This new architecture builds multi-modal interaction and preserves the independence of single modal representation. InterBERT is built with an image embedding layer, a text embedding layer, a single-stream interaction modu... |
Given the following machine learning model name: Magnification Prior Contrastive Similarity, provide a description of the model | Self-supervised pre-training method to learn efficient representations without labels on histopathology medical images utilizing magnification factors. |
Given the following machine learning model name: First Integer Neighbor Clustering Hierarchy (FINCH)), provide a description of the model | FINCH is a parameter-free fast and scalable clustering algorithm. it stands out for its speed and clustering quality. |
Given the following machine learning model name: Movement Pruning, provide a description of the model | **Movement Pruning** is a simple, deterministic first-order weight pruning method that is more adaptive to pretrained model fine-tuning. Magnitude pruning can be seen as utilizing zeroth-order information (absolute value) of the running model. In contrast, movement pruning methods are where importance is derived from f... |
Given the following machine learning model name: Slanted Triangular Learning Rates, provide a description of the model | **Slanted Triangular Learning Rates (STLR)** is a learning rate schedule which first linearly increases the learning rate and then linearly decays it, which can be seen in Figure to the right. It is a modification of Triangular Learning Rates, with a short increase and a long decay period. |
Given the following machine learning model name: Network Dissection, provide a description of the model | **Network Dissection** is an interpretability method for [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks) that evaluates the alignment between individual hidden units and a set of visual semantic concepts. By identifying the best alignments, units are given human interpretable labels ac... |
Given the following machine learning model name: Dense Prediction Transformer, provide a description of the model | **Dense Prediction Transformers** (DPT) are a type of [vision transformer](https://paperswithcode.com/method/vision-transformer) for dense prediction tasks.
The input image is transformed into tokens (orange) either by extracting non-overlapping patches followed by a linear projection of their flattened representati... |
Given the following machine learning model name: Adversarial Latent Autoencoder, provide a description of the model | **ALAE**, or **Adversarial Latent Autoencoder**, is a type of autoencoder that attempts to overcome some of the limitations of[ generative adversarial networks](https://paperswithcode.com/paper/generative-adversarial-networks). The architecture allows the latent distribution to be learned from data to address entanglem... |
Given the following machine learning model name: Associative LSTM, provide a description of the model | An **Associative LSTM** combines an [LSTM](https://paperswithcode.com/method/lstm) with ideas from Holographic Reduced Representations (HRRs) to enable key-value storage of data. HRRs use a “binding” operator to implement key-value
binding between two vectors (the key and its associated content). They natively impleme... |
Given the following machine learning model name: Feature Selection, provide a description of the model | Feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. |
Given the following machine learning model name: FreeAnchor, provide a description of the model | **FreeAnchor** is an anchor supervision method for object detection. Many CNN-based object detectors assign anchors for ground-truth objects under the restriction of object-anchor Intersection-over-Unit (IoU). In contrast, FreeAnchor is a learning-to-match approach that breaks the IoU restriction, allowing objects to m... |
Given the following machine learning model name: Gaussian Affinity, provide a description of the model | **Gaussian Affinity** is a type of affinity or self-similarity function between two points $\mathbb{x\_{i}}$ and $\mathbb{x\_{j}}$ that uses a Gaussian function:
$$ f\left(\mathbb{x\_{i}}, \mathbb{x\_{j}}\right) = e^{\mathbb{x^{T}\_{i}}\mathbb{x\_{j}}} $$
Here $\mathbb{x^{T}\_{i}}\mathbb{x\_{j}}$ is dot-product s... |
Given the following machine learning model name: CrossTransformers, provide a description of the model | CrossTransformers is a Transformer-based neural network architecture which can take a small number of labeled images and an unlabeled query, find coarse spatial correspondence between the query and the labeled images, and then infer class membership by computing distances between spatially-corresponding features. |
Given the following machine learning model name: Deep Voice 3, provide a description of the model | **Deep Voice 3 (DV3)** is a fully-convolutional attention-based neural text-to-speech system. The Deep Voice 3 architecture consists of three components:
- Encoder: A fully-convolutional encoder, which converts textual features to an internal
learned representation.
- Decoder: A fully-convolutional causal decode... |
Given the following machine learning model name: Randomized Adversarial Solarization, provide a description of the model | Attack on image classifiers by a image solarization through greedy random search. |
Given the following machine learning model name: High-resolution input, provide a description of the model | |
Given the following machine learning model name: Mesh-TensorFlow, provide a description of the model | **Mesh-TensorFlow** is a language for specifying a general class of distributed tensor computations. Where data-parallelism can be viewed as splitting tensors and operations along the "batch" dimension, in Mesh-TensorFlow, the user can specify any tensor dimensions to be split across any dimensions of a multi-dimension... |
Given the following machine learning model name: StereoLayers, provide a description of the model | |
Given the following machine learning model name: ManifoldPlus, provide a description of the model | **ManifoldPlus** is a method for robust and scalable conversion of triangle soups to watertight manifolds. It extracts exterior faces between occupied voxels and empty voxels, and uses a projection based optimization method to accurately recover a watertight manifold that resembles the reference mesh. It does not rely ... |
Given the following machine learning model name: Hard Sigmoid, provide a description of the model | The **Hard Sigmoid** is an activation function used for neural networks of the form:
$$f\left(x\right) = \max\left(0, \min\left(1,\frac{\left(x+1\right)}{2}\right)\right)$$
Image Source: [Rinat Maksutov](https://towardsdatascience.com/deep-study-of-a-not-very-deep-neural-network-part-2-activation-functions-fd9bd8... |
Given the following machine learning model name: S-shaped ReLU, provide a description of the model | The **S-shaped Rectified Linear Unit**, or **SReLU**, is an activation function for neural networks. It learns both convex and non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specific... |
Given the following machine learning model name: Inception-v4, provide a description of the model | **Inception-v4** is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than [Inception-v3](https://paperswithcode.com/method/inception-v3). |
Given the following machine learning model name: Talking-Heads Attention, provide a description of the model | **Talking-Heads Attention** is a variation on [multi-head attention](https://paperswithcode.com/method/multi-head-attention) which includes linear projections across the attention-heads dimension, immediately before and after the [softmax](https://paperswithcode.com/method/softmax) operation. In [multi-head attention](... |
Given the following machine learning model name: Probabilistically Masked Language Model, provide a description of the model | **Probabilistically Masked Language Model**, or **PMLM**, is a type of language model that utilizes a probabilistic masking scheme, aiming to bridge the gap between masked and autoregressive language models. The basic idea behind the connection of two categories of models is similar to MADE by Germain et al (2015). PML... |
Given the following machine learning model name: Bidirectional LSTM, provide a description of the model | A **Bidirectional LSTM**, or **biLSTM**, is a sequence processing model that consists of two LSTMs: one taking the input in a forward direction, and the other in a backwards direction. BiLSTMs effectively increase the amount of information available to the network, improving the context available to the algorithm (e.g.... |
Given the following machine learning model name: Grouped Convolution, provide a description of the model | A **Grouped Convolution** uses a group of convolutions - multiple kernels per layer - resulting in multiple channel outputs per layer. This leads to wider networks helping a network learn a varied set of low level and high level features. The original motivation of using Grouped Convolutions in [AlexNet](https://papers... |
Given the following machine learning model name: HITNet, provide a description of the model | **HITNet** is a framework for neural network based depth estimation which overcomes the computational disadvantages of operating on a 3D volume by integrating image warping, spatial propagation and a fast high resolution initialization step into the network architecture, while keeping the flexibility of a learned repre... |
Given the following machine learning model name: Discrete Cosine Transform, provide a description of the model | **Discrete Cosine Transform (DCT)** is an orthogonal transformation method that decomposes an
image to its spatial frequency spectrum. It expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. It is used a lot in compression tasks, e..g image compression ... |
Given the following machine learning model name: I-BERT, provide a description of the model | **I-BERT** is a quantized version of [BERT](https://paperswithcode.com/method/bert) that quantizes the entire inference with integer-only arithmetic. Based on lightweight integer only approximation methods for nonlinear operations, e.g., [GELU](https://paperswithcode.com/method/gelu), [Softmax](https://paperswithcode.c... |
Given the following machine learning model name: FuseFormer Block, provide a description of the model | A **FuseFormer block** is used in the [FuseFormer](https://paperswithcode.com/method/fuseformer) model for video inpainting. It is the same to standard [Transformer](https://paperswithcode.com/method/transformer) block except that feed forward network is replaced with a Fusion Feed Forward Network (F3N). F3N brings no ... |
Given the following machine learning model name: ZeRO-Infinity, provide a description of the model | **ZeRO-Infinity** is a sharded data parallel system that extends [ZeRO](https://paperswithcode.com/method/zero) with new innovations in heterogeneous memory access called the infinity offload engine. This allows ZeRO-Infinity to support massive model sizes on limited GPU resources by exploiting CPU and NVMe memory simu... |
Given the following machine learning model name: PP-YOLO, provide a description of the model | **PP-YOLO** is an object detector based on [YOLOv3](https://paperswithcode.com/method/yolov3). It mainly tries to combine various existing tricks that almost not increase the number of model parameters and FLOPs, to achieve the goal of improving the accuracy of detector as much as possible while ensuring that the speed... |
Given the following machine learning model name: Neural adjoint method, provide a description of the model | The NA method can be divided into two steps: (i) Training a neural network approximation of f , and (ii) inference of xˆ. Step (i) is conventional and involves training a generic neural network on a dataset
ˆ
of input/output pairs from the simulator, denoted D, resulting in f, an approximation of the forward ˆ
model... |
Given the following machine learning model name: XGPT, provide a description of the model | XGPT is a method of cross-modal generative pre-training for image captioning designed to pre-train text-to-image caption generators through three novel generation tasks, including image-conditioned masked language modeling (IMLM), image-conditioned denoising autoencoding (IDA), and text-conditioned image feature genera... |
Given the following machine learning model name: Adversarial Model Perturbation, provide a description of the model | Based on the understanding that the flat local minima of the empirical risk cause the model to generalize better. Adversarial Model Perturbation (AMP) improves generalization via minimizing the **AMP loss**, which is obtained from the empirical risk by applying the **worst** norm-bounded perturbation on each point in t... |
Given the following machine learning model name: UCNet, provide a description of the model | **UCNet** is a probabilistic framework for RGB-D Saliency Detection that employs uncertainty by learning from the data labelling process. It utilizes conditional variational autoencoders to model human annotation uncertainty and generate multiple saliency maps for each input image by sampling in the latent space. |
Given the following machine learning model name: Depthwise Fire Module, provide a description of the model | A **Depthwise Fire Module** is a modification of a [Fire Module](https://paperswithcode.com/method/fire-module) with depthwise separable convolutions to improve the inference time performance. It is used in the [CornerNet](https://paperswithcode.com/method/cornernet)-Lite architecture for object detection. |
Given the following machine learning model name: Deep-CAPTCHA, provide a description of the model | |
Given the following machine learning model name: AugMix, provide a description of the model | AugMix mixes augmented images through linear interpolations. Consequently it is like [Mixup](https://paperswithcode.com/method/mixup) but instead mixes augmented versions of the same image. |
Given the following machine learning model name: Shuffle Transformer, provide a description of the model | The **Shuffle Transformer Block** consists of the Shuffle Multi-Head Self-Attention module (ShuffleMHSA), the Neighbor-Window Connection module (NWC), and the MLP module. To introduce cross-window connections while maintaining the efficient computation of non-overlapping windows, a strategy which alternates between WMS... |
Given the following machine learning model name: MuVER, provide a description of the model | **Multi-View Entity Representations**, or **MuVER**, is an approach for entity retrieval that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. It matches a mention to the appropriate entity by comparing it with entity descript... |
Given the following machine learning model name: Aging Evolution, provide a description of the model | **Aging Evolution**, or **Regularized Evolution**, is an evolutionary algorithm for [neural architecture search](https://paperswithcode.com/method/neural-architecture-search). Whereas in tournament selection, the best architectures are kept, in aging evolution we associate each genotype with an age, and bias the tourna... |
Given the following machine learning model name: Meta Pseudo Labels, provide a description of the model | **Meta Pseudo Labels** is a semi-supervised learning method that uses a teacher network to generate pseudo labels on unlabeled data to teach a student network. The teacher receives feedback from the student to inform the teacher to generate better pseudo labels. This feedback signal is used as a reward to train the tea... |
Given the following machine learning model name: Supervised Contrastive Loss, provide a description of the model | **Supervised Contrastive Loss** is an alternative loss function to cross entropy that the authors argue can leverage label information more effectively. Clusters of points belonging to the same class are pulled together in embedding space, while simultaneously pushing apart clusters of samples from different classes.
... |
Given the following machine learning model name: Adaptive Bins, provide a description of the model | |
Given the following machine learning model name: Routing Attention, provide a description of the model | **Routed Attention** is an attention pattern proposed as part of the [Routing Transformer](https://paperswithcode.com/method/routing-transformer) architecture. Each attention module
considers a clustering of the space: the current timestep only attends to context belonging to the same cluster. In other word, the curr... |
Given the following machine learning model name: Stochastically Scaling Features and Gradients Regularization, provide a description of the model | Please enter a description about the method here |
Given the following machine learning model name: PIoU Loss, provide a description of the model | **PIoU Loss** is a loss function for oriented object detection which is formulated to exploit both the angle and IoU for accurate oriented bounding box regression. The PIoU loss is derived from IoU metric with a pixel-wise form. |
Given the following machine learning model name: Self-Attention GAN, provide a description of the model | The **Self-Attention Generative Adversarial Network**, or **SAGAN**, allows for attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details ca... |
Given the following machine learning model name: Inverted Bottleneck BERT, provide a description of the model | **IB-BERT**, or **Inverted Bottleneck BERT**, is a [BERT](https://paperswithcode.com/method/bert) variant that uses an [inverted bottleneck](https://paperswithcode.com/method/inverted-residual-block) structure. It is used as a teacher network to train the [MobileBERT](https://paperswithcode.com/method/mobilebert) model... |
Given the following machine learning model name: Depthwise Dilated Separable Convolution, provide a description of the model | A **Depthwise Dilated Separable Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) that combines [depthwise separability](https://paperswithcode.com/method/depthwise-separable-convolution) with the use of [dilated convolutions](https://paperswithcode.com/method/dilated-convolution). |
Given the following machine learning model name: HyperDenseNet, provide a description of the model | Recently, [dense connections](https://paperswithcode.com/method/dense-connections) have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, [DenseNet](https://paperswithcode.com/method/densenet) that connects each layer to... |
Given the following machine learning model name: WaveTTS, provide a description of the model | **WaveTTS** is a [Tacotron](https://paperswithcode.com/method/tacotron)-based text-to-speech architecture that has two loss functions: 1) time-domain loss, denoted as the waveform loss, that measures the distortion between the natural and generated waveform; and 2) frequency-domain loss, that measures the Mel-scale aco... |
Given the following machine learning model name: Sparse Autoencoder, provide a description of the model | A **Sparse Autoencoder** is a type of autoencoder that employs sparsity to achieve an information bottleneck. Specifically the loss function is constructed so that activations are penalized within a layer. The sparsity constraint can be imposed with [L1 regularization](https://paperswithcode.com/method/l1-regularizatio... |
Given the following machine learning model name: Absolute Learning Progress and Gaussian Mixture Models for Automatic Curriculum Learning, provide a description of the model | ALP-GMM is is an algorithm that learns to generate a learning curriculum for black box reinforcement learning agents, whereby it sequentially samples parameters controlling a stochastic procedural generation of tasks or environments. |
Given the following machine learning model name: Surrogate Lagrangian Relaxation, provide a description of the model | Please enter a description about the method here |
Given the following machine learning model name: XLM-R, provide a description of the model | XLM-R |
Given the following machine learning model name: Conditional Convolutions for Instance Segmentation, provide a description of the model | CondInst is a simple yet effective instance segmentation framework. It eliminates ROI cropping and feature alignment with the instance-aware mask heads. As a result, CondInst can solve instance segmentation with fully convolutional networks. CondInst is able to produce high-resolution instance masks without longer comp... |
Given the following machine learning model name: Multi-Heads of Mixed Attention, provide a description of the model | The multi-head of mixed attention combines both self- and cross-attentions, encouraging high-level learning of interactions between entities captured in the various attention features. It is build with several attention heads, each of the head can implement either self or cross attention. A self attention is when the k... |
Given the following machine learning model name: SqueezeNeXt, provide a description of the model | **SqueezeNeXt** is a type of convolutional neural network that uses the [SqueezeNet](https://paperswithcode.com/method/squeezenet) architecture as a baseline, but makes a number of changes. First, a more aggressive channel reduction is used by incorporating a two-stage squeeze module. This significantly reduces the tot... |
Given the following machine learning model name: Style-based Recalibration Module, provide a description of the model | A **Style-based Recalibration Module (SRM)** is a module for convolutional neural networks that adaptively recalibrates intermediate feature maps by exploiting their styles. SRM first extracts the style information from each channel of the feature maps by style pooling, then estimates per-channel recalibration weight v... |
Given the following machine learning model name: Jigsaw, provide a description of the model | **Jigsaw** is a self-supervision approach that relies on jigsaw-like puzzles as the pretext task in order to learn image representations. |
Given the following machine learning model name: K-Net, provide a description of the model | **K-Net** is a framework for unified semantic and instance segmentation that segments both instances and semantic categories consistently by a group of learnable kernels, where each kernel is responsible for generating a mask for either a potential instance or a stuff class. It begins with a set of kernels that are ran... |
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