8,725 machine learning methods and techniques
LeViT Attention Block is a module used for attention in the LeViT architecture. Its main feature is providing positional information within each attention block, i.e. where we explicitly inject relative position information in the attention mechanism. This is achieved by adding an attention bias to the attention maps.
time-causal and time-recursive scale-space representation
The time-causal and time-recursive scale-space representation is obtained by filtering any 1-D signal with the time-causal limit kernel, and provides a way to define a multi-scale analysis for signals, for which the future cannot be accessed and additionally the computations should be strictly time-recursive, in order to not require any complementary memory of the past beyond the temporal scale-space representation itself.
DVD-GAN DBlock is a residual block for the discriminator used in the DVD-GAN architecture for video generation. Unlike regular residual blocks, 3D convolutions are employed due to the application to multiple frames in a video.
Spatio-Temporal Attention LSTM
In human action recognition, each type of action generally only depends on a few specific kinematic joints. Furthermore, over time, multiple actions may be performed. Motivated by these observations, Song et al. proposed a joint spatial and temporal attention network based on LSTM, to adaptively find discriminative features and keyframes. Its main attention-related components are a spatial attention sub-network, to select important regions, and a temporal attention sub-network, to select key frames. The spatial attention sub-network can be written as: \begin{align} s{t} &= U{s}\tanh(W{xs}X{t} + W{hs}h{t-1}^{s} + b{si}) + b{so} \end{align} \begin{align} \alpha{t} &= \text{Softmax}(s{t}) \end{align} \begin{align} Y{t} &= \alpha{t} X{t} \end{align} where is the input feature at time , , , , and are learnable parameters, and is the hidden state at step . Note that use of the hidden state means the attention process takes temporal relationships into consideration. The temporal attention sub-network is similar to the spatial branch and produces its attention map using: \begin{align} \beta{t} = \delta(W{xp}X{t} + W{hp}h{t-1}^{p} + b{p}). \end{align} It adopts a ReLU function instead of a normalization function for ease of optimization. It also uses a regularized objective function to improve convergence. Overall, this paper presents a joint spatiotemporal attention method to focus on important joints and keyframes, with excellent results on the action recognition task.
Locally-Grouped Self-Attention, or LSA, is a local attention mechanism used in the Twins-SVT architecture. Locally-grouped self-attention (LSA). Motivated by the group design in depthwise convolutions for efficient inference, we first equally divide the 2D feature maps into sub-windows, making self-attention communications only happen within each sub-window. This design also resonates with the multi-head design in self-attention, where the communications only occur within the channels of the same head. To be specific, the feature maps are divided into sub-windows. Without loss of generality, we assume and . Each group contains elements, and thus the computation cost of the self-attention in this window is , and the total cost is . If we let and , the cost can be computed as , which is significantly more efficient when and and grows linearly with if and are fixed. Although the locally-grouped self-attention mechanism is computation friendly, the image is divided into non-overlapping sub-windows. Thus, we need a mechanism to communicate between different sub-windows, as in Swin. Otherwise, the information would be limited to be processed locally, which makes the receptive field small and significantly degrades the performance as shown in our experiments. This resembles the fact that we cannot replace all standard convolutions by depth-wise convolutions in CNNs.
GBlock is a type of residual block used in the GAN-TTS text-to-speech architecture - it is a stack of two residual blocks. As the generator is producing raw audio (e.g. a 2s training clip corresponds to a sequence of 48000 samples), dilated convolutions are used to ensure that the receptive field of is large enough to capture long-term dependencies. The four kernel size-3 convolutions in each GBlock have increasing dilation factors: 1, 2, 4, 8. Convolutions are preceded by Conditional Batch Normalisation, conditioned on the linear embeddings of the noise term in the single-speaker case, or the concatenation of and a one-hot representation of the speaker ID in the multi-speaker case. The embeddings are different for each BatchNorm instance. A GBlock contains two skip connections, the first of which in GAN-TTS performs upsampling if the output frequency is higher than the input, and it also contains a size-1 convolution if the number of output channels is different from the input.
Phish: A Novel Hyper-Optimizable Activation Function
Deep-learning models estimate values using backpropagation. The activation function within hidden layers is a critical component to minimizing loss in deep neural-networks. Rectified Linear (ReLU) has been the dominant activation function for the past decade. Swish and Mish are newer activation functions that have shown to yield better results than ReLU given specific circumstances. Phish is a novel activation function proposed here. It is a composite function defined as f(x) = xTanH(GELU(x)), where no discontinuities are apparent in the differentiated graph on the domain observed. Generalized networks were constructed using different activation functions. SoftMax was the output function. Using images from MNIST and CIFAR-10 databanks, these networks were trained to minimize sparse categorical crossentropy. A large scale cross-validation was simulated using stochastic Markov chains to account for the law of large numbers for the probability values. Statistical tests support the research hypothesis stating Phish could outperform other activation functions in classification. Future experiments would involve testing Phish in unsupervised learning algorithms and comparing it to more activation functions.
RIFE, or Real-time Intermediate Flow Estimation is an intermediate flow estimation algorithm for Video Frame Interpolation (VFI). Many recent flow-based VFI methods first estimate the bi-directional optical flows, then scale and reverse them to approximate intermediate flows, leading to artifacts on motion boundaries. RIFE uses a neural network named IFNet that can directly estimate the intermediate flows from coarse-to-fine with much better speed. It introduces a privileged distillation scheme for training intermediate flow model, which leads to a large performance improvement. In RIFE training, given two input frames , we directly feed them into the IFNet to approximate intermediate flows and the fusion map . During training phase, a privileged teacher refines student's results to get and based on ground truth . The student model and the teacher model are jointly trained from scratch using the reconstruction loss. The teacher's approximations are more accurate so that they can guide the student to learn.
Area Under the ROC Curve for Clustering
The area under the receiver operating characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a number of studies to evaluate and compare the performance of different classifiers. In this work, we explore AUC as a performance measure in the unsupervised learning domain, more specifically, in the context of cluster analysis. In particular, we elaborate on the use of AUC as an internal/relative measure of clustering quality, which we refer to as Area Under the Curve for Clustering (AUCC). We show that the AUCC of a given candidate clustering solution has an expected value under a null model of random clustering solutions, regardless of the size of the dataset and, more importantly, regardless of the number or the (im)balance of clusters under evaluation. In addition, we elaborate on the fact that, in the context of internal/relative clustering validation as we consider, AUCC is actually a linear transformation of the Gamma criterion from Baker and Hubert (1975), for which we also formally derive a theoretical expected value for chance clusterings. We also discuss the computational complexity of these criteria and show that, while an ordinary implementation of Gamma can be computationally prohibitive and impractical for most real applications of cluster analysis, its equivalence with AUCC actually unveils a much more efficient algorithmic procedure. Our theoretical findings are supported by experimental results. These results show that, in addition to an effective and robust quantitative evaluation provided by AUCC, visual inspection of the ROC curves themselves can be useful to further assess a candidate clustering solution from a broader, qualitative perspective as well.
DELG is a convolutional neural network for image retrieval that combines generalized mean pooling for global features and attentive selection for local features. The entire network can be learned end-to-end by carefully balancing the gradient flow between two heads – requiring only image-level labels. This allows for efficient inference by extracting an image’s global feature, detected keypoints and local descriptors within a single model. The model is enabled by leveraging hierarchical image representations that arise in CNNs, which are coupled to generalized mean pooling and attentive local feature detection. Secondly, a convolutional autoencoder module is adopted that can successfully learn low-dimensional local descriptors. This can be readily integrated into the unified model, and avoids the need of post-processing learning steps, such as PCA, that are commonly used. Finally, a procedure is used that enables end-to-end training of the proposed model using only image-level supervision. This requires carefully controlling the gradient flow between the global and local network heads during backpropagation, to avoid disrupting the desired representations.
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Laplacian Pyramid Network
LapStyle, or Laplacian Pyramid Network, is a feed-forward style transfer method. It uses a Drafting Network to transfer global style patterns in low-resolution, and adopts higher resolution Revision Networks to revise local styles in a pyramid manner according to outputs of multi-level Laplacian filtering of the content image. Higher resolution details can be generated by stacking Revision Networks with multiple Laplacian pyramid levels. The final stylized image is obtained by aggregating outputs of all pyramid levels. Specifically, we first generate image pyramid from content image with the help of Laplacian filter. Rough low-resolution stylized image are then generated by the Drafting Network. Then the Revision Network generates stylized detail image in high resolution. Then the final stylized image is generated by aggregating the outputs pyramid. and in an image represent Laplacian, concatenate and aggregation operation separately.
Fast-OCR is a new lightweight detection network that incorporates features from existing models focused on the speed/accuracy trade-off, such as YOLOv2, CR-NET, and Fast-YOLOv4.
NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video
NeuralRecon is a framework for real-time 3D scene reconstruction from a monocular video. Unlike previous methods that estimate single-view depth maps separately on each key-frame and fuse them later, NeuralRecon proposes to directly reconstruct local surfaces represented as sparse TSDF volumes for each video fragment sequentially by a neural network. A learning-based TSDF fusion module based on gated recurrent units is used to guide the network to fuse features from previous fragments. This design allows the network to capture local smoothness prior and global shape prior of 3D surfaces.
Introduced by Hinton et al. in 2012, dropout has stood the test of time as a regularizer for preventing overfitting in neural networks. In this study, we demonstrate that dropout can also mitigate underfitting when used at the start of training. During the early phase, we find dropout reduces the directional variance of gradients across mini-batches and helps align the mini-batch gradients with the entire dataset's gradient. This helps counteract the stochasticity of SGD and limit the influence of individual batches on model training. Our findings lead us to a solution for improving performance in underfitting models - early dropout: dropout is applied only during the initial phases of training, and turned off afterwards. Models equipped with early dropout achieve lower final training loss compared to their counterparts without dropout. Additionally, we explore a symmetric technique for regularizing overfitting models - late dropout, where dropout is not used in the early iterations and is only activated later in training. Experiments on ImageNet and various vision tasks demonstrate that our methods consistently improve generalization accuracy. Our results encourage more research on understanding regularization in deep learning and our methods can be useful tools for future neural network training, especially in the era of large data. Code is available at https://github.com/facebookresearch/dropout .
ResNet-RS is a family of ResNet architectures that are 1.7x faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet. The authors propose two new scaling strategies: (1) scale model depth in regimes where overfitting can occur (width scaling is preferable otherwise); (2) increase image resolution more slowly than previously recommended. Additional improvements include the use of a cosine learning rate schedule, label smoothing, stochastic depth, RandAugment, decreased weight decay, squeeze-and-excitation and the use of the ResNet-D architecture.
Graph Neural Networks with Continual Learning
Although significant effort has been applied to fact-checking, the prevalence of fake news over social media, which has profound impact on justice, public trust and our society, remains a serious problem. In this work, we focus on propagation-based fake news detection, as recent studies have demonstrated that fake news and real news spread differently online. Specifically, considering the capability of graph neural networks (GNNs) in dealing with non-Euclidean data, we use GNNs to differentiate between the propagation patterns of fake and real news on social media. In particular, we concentrate on two questions: (1) Without relying on any text information, e.g., tweet content, replies and user descriptions, how accurately can GNNs identify fake news? Machine learning models are known to be vulnerable to adversarial attacks, and avoiding the dependence on text-based features can make the model less susceptible to the manipulation of advanced fake news fabricators. (2) How to deal with new, unseen data? In other words, how does a GNN trained on a given dataset perform on a new and potentially vastly different dataset? If it achieves unsatisfactory performance, how do we solve the problem without re-training the model on the entire data from scratch? We study the above questions on two datasets with thousands of labelled news items, and our results show that: (1) GNNs can achieve comparable or superior performance without any text information to state-of-the-art methods. (2) GNNs trained on a given dataset may perform poorly on new, unseen data, and direct incremental training cannot solve the problem---this issue has not been addressed in the previous work that applies GNNs for fake news detection. In order to solve the problem, we propose a method that achieves balanced performance on both existing and new datasets, by using techniques from continual learning to train GNNs incrementally.
S-shaped ReLU
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. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters. The SReLU is defined as a mapping: where , and are learnable parameters of the network and indicates that the SReLU can differ in different channels. The parameter represents the slope of the right line with input above a set threshold. and are thresholds in positive and negative directions respectively. Source: Activation Functions
Context-aware Visual Attention-based (CoVA) webpage object detection pipeline
Context-Aware Visual Attention-based end-to-end pipeline for Webpage Object Detection (CoVA) aims to learn function f to predict labels y = [] for a webpage containing N elements. The input to CoVA consists of: 1. a screenshot of a webpage, 2. list of bounding boxes [x, y, w, h] of the web elements, and 3. neighborhood information for each element obtained from the DOM tree. This information is processed in four stages: 1. the graph representation extraction for the webpage, 2. the Representation Network (RN), 3. the Graph Attention Network (GAT), and 4. a fully connected (FC) layer. The graph representation extraction computes for every web element i its set of K neighboring web elements . The RN consists of a Convolutional Neural Net (CNN) and a positional encoder aimed to learn a visual representation for each web element i ∈ {1, ..., N}. The GAT combines the visual representation of the web element i to be classified and those of its neighbors, i.e., ∀k ∈ to compute the contextual representation for web element i. Finally, the visual and contextual representations of the web element are concatenated and passed through the FC layer to obtain the classification output.
Big-Little Net is a convolutional neural network architecture for learning multi-scale feature representations. This is achieved by using a multi-branch network, which has different computational complexity at different branches with different resolutions. Through frequent merging of features from branches at distinct scales, the model obtains multi-scale features while using less computation. It consists of Big-Little Modules, which have two branches: each of which represents a separate block from a deep model and a less deep counterpart. The two branches are fused with linear combination + unit weights. These two branches are known as Big-Branch (more layers and channels at low resolutions) and Little-Branch (fewer layers and channels at high resolution).
Decomposition-Integration Class Activation Map
DecomCAM decomposes intermediate activation maps into orthogonal features using singular value decomposition and generates saliency maps by integrating them.
Lambda layers are a building block for modeling long-range dependencies in data. They consist of long-range interactions between a query and a structured set of context elements at a reduced memory cost. Lambda layers transform each available context into a linear function, termed a lambda, which is then directly applied to the corresponding query. Whereas self-attention defines a similarity kernel between the query and the context elements, a lambda layer instead summarizes contextual information into a fixed-size linear function (i.e. a matrix), thus bypassing the need for memory-intensive attention maps.
Adversarial Graph Contrastive Learning
Bilateral Guided Aggregation Layer is a feature fusion layer for semantic segmentation that aims to enhance mutual connections and fuse different types of feature representation. It was used in the BiSeNet V2 architecture. Specifically, within the BiSeNet implementation, the layer was used to employ the contextual information of the Semantic Branch to guide the feature response of Detail Branch. With different scale guidance, different scale feature representations can be captured, which inherently encodes the multi-scale information.
M2Det is a one-stage object detection model that utilises a Multi-Level Feature Pyramid Network (MLFPN) to extract features from the input image, and then similar to SSD, produces dense bounding boxes and category scores based on the learned features, followed by the non-maximum suppression (NMS) operation to produce the final results.
DALL·E 2 is a generative text-to-image model made up of two main components: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding.
Quasi-Hyperbolic Momentum (QHM) is a stochastic optimization technique that alters momentum SGD with a momentum step, averaging an SGD step with a momentum step: The authors suggest a rule of thumb of and .
Hybrid Air-Water Temperature Difference
The hybrid model couples existing macro-meteorological models developed for similar microclimates along with some minimal amount of locally-acquired meteorological and data. The hybrid model framework consists of two components, a baseline macro-meteorological model and a machine learning model trained on that baseline macro-meteorological model’s residual error over the locally-acquired training measurements.
Polynomial Convolution
PolyConv learns continuous distributions as the convolutional filters to share the weights across different vertices of graphs or points of point clouds.
Mechanism Transfer is a meta-distributional scenario for few-shot domain adaptation in which a data generating mechanism is invariant across domains. This transfer assumption can accommodate nonparametric shifts resulting in apparently different distributions while providing a solid statistical basis for domain adaptation.
Neural Network Compression Framework
Neural Network Compression Framework, or NNCF, is a Python-based framework for neural network compression with fine-tuning. It leverages recent advances of various network compression methods and implements some of them, namely quantization, sparsity, filter pruning and binarization. These methods allow producing more hardware-friendly models that can be efficiently run on general-purpose hardware computation units (CPU, GPU) or specialized deep learning accelerators.
ProxylessNet-Mobile is a convolutional neural architecture learnt with the ProxylessNAS neural architecture search algorithm that is optimized for mobile devices. It uses inverted residual blocks (MBConvs) from MobileNetV2 as its basic building block.
Contextual Attention Block
The Contextual Attention Block (CAB) is a new plug-and-play module to model context awareness. It is simple and effective and can be integrated with any feed-forward neural network. CAB infers weights that multiply the feature maps according to their causal influence on the scene, modeling the co-occurrence of different objects in the image. You can place the CAB module at different bottlenecks to infuse a hierarchical context awareness into the model.
Problem Agnostic Speech Encoder +
PASE+ is a problem-agnostic speech encoder that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). An online speech distortion module is employed, that contaminates the input signals with a variety of random disturbances. A revised encoder is also proposed that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, the authors refine the set of workers used in self-supervision to encourage better cooperation.
Step-wise Direct Preference Optimization
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DeepViT is a type of vision transformer that replaces the self-attention layer within the transformer block with a Re-attention module to address the issue of attention collapse and enables training deeper ViTs.
Frequency channel attention networks
FCANet contains a novel multi-spectral channel attention module. Given an input feature map , multi-spectral channel attention first splits into many parts . Then it applies a 2D DCT to each part . Note that a 2D DCT can use pre-processing results to reduce computation. After processing each part, all results are concatenated into a vector. Finally, fully connected layers, ReLU activation and a sigmoid are used to get the attention vector as in an SE block. This can be formulated as: \begin{align} s = F\text{fca}(X, \theta) & = \sigma (W{2} \delta (W{1}[(\text{DCT}(\text{Group}(X)))])) \end{align} \begin{align} Y & = s X \end{align} where indicates dividing the input into many groups and is the 2D discrete cosine transform. This work based on information compression and discrete cosine transforms achieves excellent performance on the classification task.
Liu et al. presented self-calibrated convolution as a means to enlarge the receptive field at each spatial location. Self-calibrated convolution is used together with a standard convolution. It first divides the input feature into and in the channel domain. The self-calibrated convolution first uses average pooling to reduce the input size and enlarge the receptive field: \begin{align} T{1} = AvgPool{r}(X{1}) \end{align} where is the filter size and stride. Then a convolution is used to model the channel relationship and a bilinear interpolation operator is used to upsample the feature map: \begin{align} X'{1} = \text{Up}(Conv2(T1)) \end{align} Next, element-wise multiplication finishes the self-calibrated process: \begin{align} Y'{1} = Conv3(X1) \sigma(X1 + X'1) \end{align} Finally, the output feature map of is formed: \begin{align} Y{1} &= Conv4(Y'{1}) \end{align} \begin{align} Y2 &= Conv1(X2) \end{align} \begin{align} Y &= [Y1; Y2] \end{align} Such self-calibrated convolution can enlarge the receptive field of a network and improve its adaptability. It achieves excellent results in image classification and certain downstream tasks such as instance segmentation, object detection and keypoint detection.
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Margin Rectified Linear Unit
Margin Rectified Linear Unit, or Margin ReLU, is a type of activation function based on a ReLU, but it has a negative threshold for negative values instead of a zero threshhold.
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CTAB-GAN is a model for conditional tabular data generation. The generator and discriminator utilize the DCGAN architecture. An auxiliary classifier is also used with an MLP architecture.
ACKTR, or Actor Critic with Kronecker-factored Trust Region, is an actor-critic method for reinforcement learning that applies trust region optimization using a recently proposed Kronecker-factored approximation to the curvature. The method extends the framework of natural policy gradient and optimizes both the actor and the critic using Kronecker-factored approximate curvature (K-FAC) with trust region.
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An All-Attention Layer is an attention module and layer for transformers that merges the self-attention and feedforward sublayers into a single unified attention layer. As opposed to the two-step mechanism of the Transformer layer, it directly builds its representation from the context and a persistent memory block without going through a feedforward transformation. The additional persistent memory block stores, in the form of key-value vectors, information that does not depend on the context. In terms of parameters, these persistent key-value vectors replace the feedforward sublayer.
YOLOP is a panoptic driving perception network for handling traffic object detection, drivable area segmentation and lane detection simultaneously. It is composed of one encoder for feature extraction and three decoders to handle the specific tasks. It can be thought of a lightweight version of Tesla's HydraNet model for self-driving cars. A lightweight CNN, from Scaled-yolov4, is used as the encoder to extract features from the image. Then these feature maps are fed to three decoders to complete their respective tasks. The detection decoder is based on the current best-performing single-stage detection network, YOLOv4, for two main reasons: (1) The single-stage detection network is faster than the two-stage detection network. (2) The grid-based prediction mechanism of the single-stage detector is more related to the other two semantic segmentation tasks, while instance segmentation is usually combined with the region based detector as in Mask R-CNN. The feature map output by the encoder incorporates semantic features of different levels and scales, and our segmentation branch can use these feature maps to complete pixel-wise semantic prediction.
AccoMontage is a model for accompaniment arrangement, a type of music generation task involving intertwined constraints of melody, harmony, texture, and music structure. AccoMontage generates piano accompaniments for folk/pop songs based on a lead sheet (i.e. a melody with chord progression). It first retrieves phrase montages from a database while recombining them structurally using dynamic programming. Second, chords of the retrieved phrases are manipulated to match the lead sheet via style transfer. Lastly, the system offers controls over the generation process. In contrast to pure deep learning approaches, AccoMontage uses a hybrid pathway, in which rule-based optimization and deep learning are both leveraged.
Log-time and Log-space Extreme Classification
LTLS is a technique for multiclass and multilabel prediction that can perform training and inference in logarithmic time and space. LTLS embeds large classification problems into simple structured prediction problems and relies on efficient dynamic programming algorithms for inference. It tackles extreme multi-class and multi-label classification problems where the size of the output space is extremely large.