8,725 machine learning methods and techniques
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 relation module and a channel squeeze layer to improve the INN nonlinearity to extract cross-image relations and the network flexibility, respectively.
MinCut Pooling
MinCutPool is a trainable pooling operator for graphs that learns to map nodes into clusters. The method is trained to approximate the minimum K-cut of the graph to ensure that the clusters are balanced, while also jointly optimizing the objective of the task at hand.
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Batch Transformer
learn to explore the sample relationships via transformer networks
Adaptive Early-Learning Correction
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations
U-Net Generative Adversarial Network
In contrast to typical GANs, a U-Net GAN uses a segmentation network as the discriminator. This segmentation network predicts two classes: real and fake. In doing so, the discriminator gives the generator region-specific feedback. This discriminator design also enables a CutMix-based consistency regularization on the two-dimensional output of the U-Net GAN discriminator, which further improves image synthesis quality.
RPM-Net is an end-to-end differentiable deep network for robust point matching uses learned features. It preserves robustness of RPM against noisy/outlier points while desensitizing initialization with point correspondences from learned feature distances instead of spatial distances. The network uses the differentiable Sinkhorn layer and annealing to get soft assignments of point correspondences from hybrid features learned from both spatial coordinates and local geometry. To further improve registration performance, the authors introduce a secondary network to predict optimal annealing parameters.
A Sandwich Transformer is a variant of a Transformer that reorders sublayers in the architecture to achieve better performance. The reordering is based on the authors' analysis that models with more self-attention toward the bottom and more feedforward sublayers toward the top tend to perform better in general.
Monocular Real-Time Volumetric Performance Capture
Entropy Minimized Ensemble of Adapters
Entropy Minimized Ensemble of Adapters, or EMEA, is a method that optimizes the ensemble weights of the pretrained language adapters for each test sentence by minimizing the entropy of its predictions. The intuition behind the method is that a good adapter weight for a test input should make the model more confident in its prediction for , that is, it should lead to lower model entropy over the input
Global Sub-Sampled Attention, or GSA, is a local attention mechanism used in the Twins-SVT architecture. A single representative is used to summarize the key information for each of subwindows and the representative is used to communicate with other sub-windows (serving as the key in self-attention), which can reduce the cost to . This is essentially equivalent to using the sub-sampled feature maps as the key in attention operations, and thus it is termed global sub-sampled attention (GSA). If we alternatively use the LSA and GSA like separable convolutions (depth-wise + point-wise). The total computation cost is We have: The minimum is obtained when . Note that is popular in classification. Without loss of generality, square sub-windows are used, i.e., . Therefore, is close to the global minimum for . However, the network is designed to include several stages with variable resolutions. Stage 1 has feature maps of , the minimum is obtained when . Theoretically, we can calibrate optimal and for each of the stages. For simplicity, is used everywhere. As for stages with lower resolutions, the summarizing window-size of GSA is controlled to avoid too small amount of generated keys. Specifically, the sizes of 4,2 and 1 are used for the last three stages respectively.
Self-Supervised Temporal Domain Adaptation
Self-Supervised Temporal Domain Adaptation (SSTDA) is a method for action segmentation with self-supervised temporal domain adaptation. It contains two self-supervised auxiliary tasks (binary and sequential domain prediction) to jointly align cross-domain feature spaces embedded with local and global temporal dynamics.
Drafting Network is a style transfer module designed to transfer global style patterns in low-resolution, since global patterns can be transferred easier in low resolution due to larger receptive field and less local details. To achieve single style transfer, earlier work trained an encoder-decoder module, where only the content image is used as input. To better combine the style feature and the content feature, the Drafting Network adopts the AdaIN module. The architecture of Drafting Network is shown in the Figure, which includes an encoder, several AdaIN modules and a decoder. (1) The encoder is a pre-trained VGG-19 network, which is fixed during training. Given and , the VGG encoder extracts features in multiple granularity at 21, 31 and 41 layers. (2) Then, we apply feature modulation between the content and style feature using AdaIN modules after 21, 31 and 41 layers, respectively. (3) Finally, in each granularity of decoder, the corresponding feature from the AdaIN module is merged via a skip-connection. Here, skip-connections after AdaIN modules in both low and high levels are leveraged to help to reserve content structure, especially for low-resolution image.
The Recurrent Entity Network is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it receives new data. For language understanding tasks, it can reason on-the-fly as it reads text, not just when it is required to answer a question or respond as is the case for a Memory Network. Like a Neural Turing Machine or Differentiable Neural Computer, it maintains a fixed size memory and can learn to perform location and content-based read and write operations. However, unlike those models it has a simple parallel architecture in which several memory locations can be updated simultaneously. The model consists of a fixed number of dynamic memory cells, each containing a vector key and a vector value (or content) . Each cell is associated with its own processor, a simple gated recurrent network that may update the cell value given an input. If each cell learns to represent a concept or entity in the world, one can imagine a gating mechanism that, based on the key and content of the memory cells, will only modify the cells that concern the entities mentioned in the input. There is no direct interaction between the memory cells, hence the system can be seen as multiple identical processors functioning in parallel, with distributed local memory. The sharing of these parameters reflects an invariance of these laws across object instances, similarly to how the weight tying scheme in a CNN reflects an invariance of image statistics across locations. Their hidden state is updated only when new information relevant to their concept is received, and remains otherwise unchanged. The keys used in the addressing/gating mechanism also correspond to concepts or entities, but are modified only during learning, not during inference.
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-dimensional mesh of processors. A MeshTensorFlow graph compiles into a SPMD program consisting of parallel operations coupled with collective communication primitives such as Allreduce.
Asymmetrical Bi-RNN
An aspect of Bi-RNNs that could be undesirable is the architecture's symmetry in both time directions. Bi-RNNs are often used in natural language processing, where the order of the words is almost exclusively determined by grammatical rules and not by temporal sequentiality. However, in some cases, the data has a preferred direction in time: the forward direction. Another potential drawback of Bi-RNNs is that their output is simply the concatenation of two naive readings of the input in both directions. In consequence, Bi-RNNs never actually read an input by knowing what happens in the future. Conversely, the idea behind U-RNN, is to first do a backward pass, and then use during the forward pass information about the future. We accumulate information while knowing which part of the information will be useful in the future as it should be relevant to do so if the forward direction is the preferred direction of the data. The backward and forward hidden states and are obtained according to these equations: \begin{equation} \begin{aligned} &h{t-1}^{b}=R N N\left(h{t}^{b}, e{t}, W{b}\right) \\ &h{t+1}^{f}=R N N\left(h{t}^{f},\left[e{t}, h{t}^{b}\right], W{f}\right) \end{aligned} \end{equation} where and are learnable weights that are shared among pedestrians, and denotes concatenation. The last hidden state is then used as the encoding of the sequence.
Feature Non-Maximum Suppression, or FeatureNMS, is a post-processing step for object detection models that removes duplicates where there are multiple detections outputted per object. FeatureNMS recognizes duplicates not only based on the intersection over union between the bounding boxes, but also based on the difference of feature vectors. These feature vectors can encode more information like visual appearance.
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InstaBoost is a data augmentation technique for instance segmentation that utilises existing instance mask annotations. Intuitively in a small neighbor area of , the probability map should be high-valued since images are usually continuous and redundant in pixel level. Based on this, InstaBoost is a form of augmentation where we apply object jittering that randomly samples transformation tuples from the neighboring space of identity transform and paste the cropped object following affine transform .
SAFRAN - Scalable and fast non-redundant rule application
SAFRAN is a rule application framework which aggregates rules through a scalable clustering algorithm.
Spatially Separable Self-Attention, or SSSA, is an attention module used in the Twins-SVT architecture that aims to reduce the computational complexity of vision transformers for dense prediction tasks (given high-resolution inputs). SSSA is composed of locally-grouped self-attention (LSA) and global sub-sampled attention (GSA). Formally, spatially separable self-attention (SSSA) can be written as: where LSA means locally-grouped self-attention within a sub-window; GSA is the global sub-sampled attention by interacting with the representative keys (generated by the sub-sampling functions) from each sub-window Both LSA and GSA have multiple heads as in the standard self-attention.
Adaptive Bins
Atrous-convolution block
Atrous Convolution Neural Network (ACNN), as a pooling-free network structure, is proposed to achieve full-resolution feature processing using a theoretically optimal dilation setting for a larger receptive field, even with fewer parameters. Compared to other techniques, it can achieve higher segmentation Intersection over Union (IoU) and much less trainable parameters and model sizes, indicating the benefit of full-resolution feature maps in feature processing.
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. SMA, a synchronous variant of model averaging, is used in which replicas independently explore the solution space with gradient descent, but adjust their search synchronously based on the trajectory of a globally-consistent average model.
Continuously Differentiable Exponential Linear Units
Exponential Linear Units (ELUs) are a useful rectifier for constructing deep learning architectures, as they may speed up and otherwise improve learning by virtue of not have vanishing gradients and by having mean activations near zero. However, the ELU activation as parametrized in [1] is not continuously differentiable with respect to its input when the shape parameter alpha is not equal to 1. We present an alternative parametrization which is C1 continuous for all values of alpha, making the rectifier easier to reason about and making alpha easier to tune. This alternative parametrization has several other useful properties that the original parametrization of ELU does not: 1) its derivative with respect to x is bounded, 2) it contains both the linear transfer function and ReLU as special cases, and 3) it is scale-similar with respect to alpha.
Bi3D is a stereo depth estimation framework that estimates depth via a series of binary classifications. Rather than testing if objects are at a particular depth D, as existing stereo methods do, it classifies them as being closer or farther than D. It takes the stereo pair and a disparity and produces a confidence map, which can be thresholded to yield the binary segmentation. To estimate depth on quantization levels we run this network times and maximize the probability in Equation 8 (see paper). To estimate continuous depth, whether full or selective, we run the SegNet block of Bi3DNet for each disparity level and work directly on the confidence volume.
Symbolic Regression Large Language Models
LLM-SR pioneers the use of LLMs for scientific equation discovery and symbolic regression and shows how LLMs, with their vast scientific knowledge and coding capability, enhance equation discovery across various scientific fields.
Slope Difference Distribution Segmentation
An End-to-End Memory Network is a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network, but unlike the model in that work, it is trained end-to-end, and hence requires significantly less supervision during training. It can also be seen as an extension of RNNsearch to the case where multiple computational steps (hops) are performed per output symbol. The model takes a discrete set of inputs that are to be stored in the memory, a query , and outputs an answer . Each of the , , and contains symbols coming from a dictionary with words. The model writes all to the memory up to a fixed buffer size, and then finds a continuous representation for the and . The continuous representation is then processed via multiple hops to output .
Asynchronous Interaction Aggregation, or AIA, is a network that leverages different interactions to boost action detection. There are two key designs in it: one is the Interaction Aggregation structure (IA) adopting a uniform paradigm to model and integrate multiple types of interaction; the other is the Asynchronous Memory Update algorithm (AMU) that enables us to achieve better performance by modeling very long-term interaction dynamically.
Orientation Regularized Network
Orientation Regularized Network (ORN) is a multi-view image fusion technique for pose estimation. It uses IMU orientations as a structural prior to mutually fuse the image features of each pair of joints linked by IMUs. For example, it uses the features of the elbow to reinforce those of the wrist based on the IMU at the lower-arm.
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Noise2Fast is a model for single image blind denoising. It is similar to masking based methods -- filling in the pixel gaps -- in that the network is blind to many of the input pixels during training. The method is inspired by Neighbor2Neighbor, where the neural network learns a mapping between adjacent pixels. Noise2Fast is tuned to speed by using a discrete four image training set obtained by a form of downsampling called “checkerboard downsampling.
Multi-view Knowledge Graph Embedding
Back to the Feature
TinaFace is a type of face detection method that is based on generic object detection. It consists of (a) Feature Extractor: ResNet-50 and 6 level Feature Pyramid Network to extract the multi-scale features of input image; (b) an Inception block to enhance receptive field; (c) Classification Head: 5 layers FCN for classification of anchors; (d) Regression Head: 5 layers FCN for regression of anchors to ground-truth objects boxes; (e) IoU Aware Head: a single convolutional layer for IoU prediction.
Fuzzy Rank-based Ensemble
The motive for ensembling is to utilize each of the confidence factors generated from base learners fully by mapping them into non-linear functions. One of the mapped values signifies the abidance or closeness to 1 and the other one signifies the deviation from 1. This proposed approach overcomes the shortcoming of the conventional ranking methods. The scores from base learners are mapped on two different functions having different concavities to generate non-linear fuzzy ranks and generate a fused score by combining these two ranks, which helps us to quantify the total deviation from expected. Lesser the deviation shows better confidence towards a particular class. The class having the lowest deviation value is considered as the winner and is assigned as the final class value. Here, we first give a brief overview of the pre-trained CNN models used as base learners.
Feature-Aligned Person Search Network
AlignPS, or Feature-Aligned Person Search Network, is an anchor-free framework for efficient person search. The model employs the typical architecture of an anchor-free detection model (i.e., FCOS). An aligned feature aggregation (AFA) module is designed to make the model focus more on the re-id subtask. Specifically, AFA reshapes some building blocks of FPN to overcome the issues of region and scale misalignment in re-id feature learning. A deformable convolution is exploited to make the re-id embeddings adaptively aligned with the foreground regions. A feature fusion scheme is designed to better aggregate features from different FPN levels, which makes the re-id features more robust to scale variations. The training procedures of re-id and detection are also optimized to place more emphasis on generating robust re-id embeddings.
Funnel Transformer is a type of Transformer that gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. By re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, the model capacity is further improved. In addition, to perform token-level predictions as required by common pretraining objectives, Funnel-transformer is able to recover a deep representation for each token from the reduced hidden sequence via a decoder. The proposed model keeps the same overall skeleton of interleaved S-Attn and P-FFN sub-modules wrapped by residual connection and layer normalization. But differently, to achieve representation compression and computation reduction, THE model employs an encoder that gradually reduces the sequence length of the hidden states as the layer gets deeper. In addition, for tasks involving per-token predictions like pretraining, a simple decoder is used to reconstruct a full sequence of token-level representations from the compressed encoder output. Compression is achieved via a pooling operation,
Class Activation Guided Attention Mechanism (CAGAM)
CAGAM is a form of spatial attention mechanism that propagates attention from a known to an unknown context features thereby enhancing the unknown context for relevant pattern discovery. Usually the known context feature is a class activation map (CAM).
MultiGrain is a type of image model that learns a single embedding for classes, instances and copies. In other words, it is a convolutional neural network that is suitable for both image classification and instance retrieval. We learn MultiGrain by jointly training an image embedding for multiple tasks. The resulting representation is compact and can outperform narrowly-trained embeddings. The learned embedding output incorporates different levels of granularity.
MacBERT is a Transformer-based model for Chinese NLP that alters RoBERTa in several ways, including a modified masking strategy. Instead of masking with [MASK] token, which never appears in the fine-tuning stage, MacBERT masks the word with its similar word. Specifically MacBERT shares the same pre-training tasks as BERT with several modifications. For the MLM task, the following modifications are performed: - Whole word masking is used as well as Ngram masking strategies for selecting candidate tokens for masking, with a percentage of 40%, 30%, 20%, 10% for word-level unigram to 4-gram. - Instead of masking with [MASK] token, which never appears in the fine-tuning stage, similar words are used for the masking purpose. A similar word is obtained by using Synonyms toolkit which is based on word2vec similarity calculations. If an N-gram is selected to mask, we will find similar words individually. In rare cases, when there is no similar word, we will degrade to use random word replacement. - A percentage of 15% input words is used for masking, where 80% will replace with similar words, 10% replace with a random word, and keep with original words for the rest of 10%.
ExtremeNet is a a bottom-up object detection framework that detects four extreme points (top-most, left-most, bottom-most, right-most) of an object. It uses a keypoint estimation framework to find extreme points, by predicting four multi-peak heatmaps for each object category. In addition, it uses one heatmap per category predicting the object center, as the average of two bounding box edges in both the x and y dimension. We group extreme points into objects with a purely geometry-based approach. We group four extreme points, one from each map, if and only if their geometric center is predicted in the center heatmap with a score higher than a pre-defined threshold, We enumerate all combinations of extreme point prediction, and select the valid ones.
Packed Levitated Markers, or PL-Marker, is a span representation approach for named entity recognition that considers the dependencies between spans (pairs) by strategically packing the markers in the encoder. A pair of Levitated Markers, emphasizing a span, consists of a start marker and an end marker which share the same position embeddings with span’s start and end tokens respectively. In addition, both levitated markers adopt a restricted attention, that is, they are visible to each other, but not to the text token and other pairs of markers. sBased on the above features, the levitated marker would not affect the attended context of the original text tokens, which allows us to flexibly pack a series of related spans with their levitated markers in the encoding phase and thus model their dependencies.
SkipInit is a method that aims to allow normalization-free training of neural networks by downscaling residual branches at initialization. This is achieved by including a learnable scalar multiplier at the end of each residual branch, initialized to . The method is motivated by theoretical findings that batch normalization downscales the hidden activations on the residual branch by a factor on the order of the square root of the network depth (at initialization). Therefore, as the depth of a residual network is increased, the residual blocks are increasingly dominated by the skip connection, which drives the functions computed by residual blocks closer to the identity, preserving signal propagation and ensuring well-behaved gradients. This leads to the proposed method which can achieve this property through an initialization strategy rather than a normalization strategy.
Adversarial Latent Autoencoder
ALAE, or Adversarial Latent Autoencoder, is a type of autoencoder that attempts to overcome some of the limitations of generative adversarial networks. The architecture allows the latent distribution to be learned from data to address entanglement (A). The output data distribution is learned with an adversarial strategy (B). Thus, we retain the generative properties of GANs, as well as the ability to build on the recent advances in this area. For instance, we can include independent sources of stochasticity, which have proven essential for generating image details, or can leverage recent improvements on GAN loss functions, regularization, and hyperparameters tuning. Finally, to implement (A) and (B), AE reciprocity is imposed in the latent space (C). Therefore, we can avoid using reconstruction losses based on simple norm that operates in data space, where they are often suboptimal, like for the image space. Since it works on the latent space, rather than autoencoding the data space, the approach is named Adversarial Latent Autoencoder (ALAE).
MotionNet is a system for joint perception and motion prediction based on a bird's eye view (BEV) map, which encodes the object category and motion information from 3D point clouds in each grid cell. MotionNet takes a sequence of LiDAR sweeps as input and outputs the bird's eye view (BEV) map. The backbone of MotionNet is a spatio-temporal pyramid network, which extracts deep spatial and temporal features in a hierarchical fashion. To enforce the smoothness of predictions over both space and time, the training of MotionNet is further regularized with novel spatial and temporal consistency losses.
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Learning From Multiple Experts
Learning From Multiple Experts is a self-paced knowledge distillation framework that aggregates the knowledge from multiple 'Experts' to learn a unified student model. Specifically, the proposed framework involves two levels of adaptive learning schedules: Self-paced Expert Selection and Curriculum Instance Selection, so that the knowledge is adaptively transferred to the 'Student'. The self-paced expert selection automatically controls the impact of knowledge distillation from each expert, so that the learned student model will gradually acquire the knowledge from the experts, and finally exceed the expert. The curriculum instance selection, on the other hand, designs a curriculum for the unified model where the training samples are organized from easy to hard, so that the unified student model will receive a less challenging learning schedule, and gradually learns from easy to hard samples.
Elastic Dense Block is a skip connection block that modifies the Dense Block with downsamplings and upsamplings in parallel branches at each layer to let the network learn from a data scaling policy in which inputs are processed at different resolutions in each layer. It is called "elastic" because each layer in the network is flexible in terms of choosing the best scale by a soft policy.