8,725 machine learning methods and techniques
Model-Free Episodic Control
Non-parametric approximation of Q-values by storing all visited states and doing inference through k-Nearest Neighbors.
Channel & spatial attention combines the advantages of channel attention and spatial attention. It adaptively selects both important objects and regions
The Mogrifier LSTM is an extension to the LSTM where the LSTM’s input is gated conditioned on the output of the previous step . Next, the gated input is used in a similar manner to gate the output of the previous time step. After a couple of rounds of this mutual gating, the last updated and are fed to an LSTM. In detail, the Mogrifier is an LSTM where two inputs and modulate one another in an alternating fashion before the usual LSTM computation takes place. That is: where the modulated inputs and are defined as the highest indexed and , respectively, from the interleaved sequences: with and . The number of "rounds", , is a hyperparameter; recovers the LSTM. Multiplication with the constant 2 ensures that randomly initialized , matrices result in transformations close to identity. To reduce the number of additional model parameters, we typically factorize the , matrices as products of low-rank matrices: = with , , , where is the rank.
Generalizable SAM
The Segment Anything Model (SAM) shows remarkable segmentation ability with sparse prompts like points. However, manual prompt is not always feasible, as it may not be accessible in real-world application. In this work, we aim to eliminate the need for manual prompt.The key idea is to employ Cross-modal Chains of Thought Prompting (CCTP) to reason visual prompts using the semantic information given by a generic text prompt. We introduce a test-time adaptation per-instance mechanism called Generalizable SAM (GenSAM) to automatically generate and optimize visual prompts the generic task prompt. CCTP maps a single generic text prompt onto image-specific consensus foreground and background heatmaps using vision-language models, acquiring reliable visual prompts. Moreover, to test-time adapt the visual prompts, we further propose Progressive Mask Generation (PMG) to iteratively reweight the input image, guiding the model to focus on the targets in a coarse-to-fine manner.Crucially, all network parameters are fixed, avoiding the need for additional training.Experiments demonstrate the superiority of GenSAM. Experiments on three benchmarks demonstrate that GenSAM outperforms point supervision approaches and achieves comparable results to scribble supervision ones, solely relying on general task descriptions as prompts.
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CascadePSP is a general segmentation refinement model that refines any given segmentation from low to high resolution. The model takes as input an initial mask that can be an output of any algorithm to provide a rough object location. Then the CascadePSP will output a refined mask. The model is designed in a cascade fashion that generates refined segmentation in a coarse-to-fine manner. Coarse outputs from the early levels predict object structure which will be used as input to the latter levels to refine boundary details.
Motion-Encoded Particle Swarm Optimization
Inflated 3D ConvNet Retina Net
Zero-bounded Log-sum-exp & Pairwise Rank-based Loss
SuperpixelGridCut, SuperpixelGridMean, SuperpixelGridMix
Karim Hammoudi, Adnane Cabani, Bouthaina Slika, Halim Benhabiles, Fadi Dornaika and Mahmoud Melkemi. SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation, arXiv:2204.08458, 2022. https://doi.org/10.48550/arxiv.2204.08458
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Fraternal Dropout is a regularization method for recurrent neural networks that trains two identical copies of an RNN (that share parameters) with different dropout masks while minimizing the difference between their (pre-softmax) predictions. This encourages the representations of RNNs to be invariant to dropout mask, thus being robust.
PP-YOLOv2 is an object detector that extends upon PP-YOLO with several refinements: - A Path Aggregation Network is included for the FPN to compose bottom-up paths. - Mish Activation functions are used. - The input size is expanded. - An IoU aware branch is calculated with a soft label format.
SEED (Scalable, Efficient, Deep-RL) is a scalable reinforcement learning agent. It utilizes an architecture that features centralized inference and an optimized communication layer. SEED adopts two state of the art distributed algorithms, IMPALA/V-trace (policy gradients) and R2D2 (Q-learning).
Ternary Weight Splitting is a ternarization approach used in BinaryBERT that exploits the flatness of ternary loss landscape as the optimization proxy of the binary model. We first train the half-sized ternary BERT to convergence, and then split both the latent full-precision weight and quantized to their binary counterparts and via the TWS operator. To inherit the performance of the ternary model after splitting, the TWS operator requires the splitting equivalency (i.e., the same output given the same input): While solution to the above equation is not unique, we constrain the latent full-precision weights after splitting to satisfy . See the paper for more details.
Residual Masking Network
It uses a segmentation network to refine feature maps, enabling the network to focus on relevant information to make correct decisions.
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Hierarchical Style Disentanglement
Hierarchical Style Disentanglement, or HiSD, aims to disentangle different styles in image-to-image translation models. It organizes the labels into a hierarchical structure, where independent tags, exclusive attributes, and disentangled styles are allocated from top to bottom. To make the styles identified to the tags and attributes, the authors carefully redesign the modules, phases, and objectives.
EdgeBoxes is an approach for generating object bounding box proposals directly from edges. Similar to segments, edges provide a simplified but informative representation of an image. In fact, line drawings of an image can accurately convey the high-level information contained in an image using only a small fraction of the information. The main insight behind the method is the observation: the number of contours wholly enclosed by a bounding box is indicative of the likelihood of the box containing an object. We say a contour is wholly enclosed by a box if all edge pixels belonging to the contour lie within the interior of the box. Edges tend to correspond to object boundaries, and as such boxes that tightly enclose a set of edges are likely to contain an object. However, some edges that lie within an object’s bounding box may not be part of the contained object. Specifically, edge pixels that belong to contours straddling the box’s boundaries are likely to correspond to objects or structures that lie outside the box. Source: Zitnick and Dollar
TorchBeast is a platform for reinforcement learning (RL) research in PyTorch. It implements a version of the popular IMPALA algorithm for fast, asynchronous, parallel training of RL agents.
Synergistic Image and Feature Alignment
Synergistic Image and Feature Alignment is an unsupervised domain adaptation framework that conducts synergistic alignment of domains from both image and feature perspectives. In SIFA, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features by leveraging adversarial learning in multiple aspects and with a deeply supervised mechanism. The feature encoder is shared between both adaptive perspectives to leverage their mutual benefits via end-to-end learning.
Multi-source Sentiment Generative Adversarial Network
Multi-source Sentiment Generative Adversarial Network is a multi-source domain adaptation (MDA) method for visual sentiment classification. It is composed of three pipelines, i.e., image reconstruction, image translation, and cycle-reconstruction. To handle data from multiple source domains, it learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. This is achieved via cycle consistent adversarial learning in an end-to-end manner. Notably, thanks to the unified sentiment latent space, MSGAN requires a single classification network to handle data from different source domains.
Adaptive Hybrid Activation Function
Trainable activation function as a sigmoid-based generalization of ReLU, Swish and SiLU.
LFPNet with test time augmentation
Difference of Gaussian Random Forest
Discriminative Adversarial Search, or DAS, is a sequence decoding approach which aims to alleviate the effects of exposure bias and to optimize on the data distribution itself rather than for external metrics. Inspired by generative adversarial networks (GANs), wherein a discriminator is used to improve the generator, DAS differs from GANs in that the generator parameters are not updated at training time and the discriminator is only used to drive sequence generation at inference time.
FashionCLIP is a fine-tuned CLIP model on fashion data (more than 800K pairs). It is the first foundation model for Fashion.
Intrinsically Motivated Goal Exploration Processes
Population-based intrinsically motivated goal exploration algorithms applied to real world robot learning of complex skills like tool use.
One Representation
In the OneR method, model input can be one of image, text or image+text, and CMC objective is combined with the traditional image-text contrastive (ITC) loss. Masked modeling is also carried out for all three input types (i.e., image, text and multi-modal). This framework employs no modality-specific architectural component except for the initial token embedding layer, making our model generic and modality-agnostic with minimal inductive bias.
AutoML-Zero is an AutoML technique that aims to search a fine-grained space simultaneously for the model, optimization procedure, initialization, and so on, permitting much less human-design and even allowing the discovery of non-neural network algorithms. It represents ML algorithms as computer programs comprised of three component functions, Setup, Predict, and Learn, that performs initialization, prediction and learning. The instructions in these functions apply basic mathematical operations on a small memory. The operation and memory addresses used by each instruction are free parameters in the search space, as is the size of the component functions. While this reduces expert design, the consequent sparsity means that random search cannot make enough progress. To overcome this difficulty, the authors use small proxy tasks and migration techniques to build an optimized infrastructure capable of searching through 10,000 models/second/cpu core. Evolutionary methods can find solutions in the AutoML-Zero search space despite its enormous size and sparsity. The authors show that by randomly modifying the programs and periodically selecting the best performing ones on given tasks/datasets, AutoML-Zero discovers reasonable algorithms. They start from empty programs and using data labeled by “teacher” neural networks with random weights, and demonstrate evolution can discover neural networks trained by gradient descent. Following this, they minimize bias toward known algorithms by switching to binary classification tasks extracted from CIFAR-10 and allowing a larger set of possible operations. This discovers interesting techniques like multiplicative interactions, normalized gradient and weight averaging. Finally, they show it is possible for evolution to adapt the algorithm to the type of task provided. For example, dropout-like operations emerge when the task needs regularization and learning rate decay appears when the task requires faster convergence.
Dual Contrastive Learning
Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to supervised learning tasks remains as a challenge in practice. In this work, we introduce a dual contrastive learning (DualCL) framework that simultaneously learns the features of input samples and the parameters of classifiers in the same space. Specifically, DualCL regards the parameters of the classifiers as augmented samples associating to different labels and then exploits the contrastive learning between the input samples and the augmented samples. Empirical studies on five benchmark text classification datasets and their low-resource version demonstrate the improvement in classification accuracy and confirm the capability of learning discriminative representations of DualCL.
Multi-layer neural networks traditionally use dot products between the output vector of previous layer and the incoming weight vector as the input to activation function. The result of dot product is unbounded. To bound dot product and decrease the variance, Cosine Normalization uses cosine similarity or centered cosine similarity (Pearson Correlation Coefficient) instead of dot products in neural networks. Using cosine normalization, the output of a hidden unit is computed by: where is the normalized pre-activation, is the incoming weight vector and is the input vector, () indicates dot product, is nonlinear activation function. Cosine normalization bounds the pre-activation between -1 and 1.
MobileDet is an object detection model developed for mobile accelerators. MobileDets uses regular convolutions extensively on EdgeTPUs and DSPs, especially in the early stage of the network where depthwise convolutions tend to be less efficient. This helps boost the latency-accuracy trade-off for object detection on accelerators, provided that they are placed strategically in the network via neural architecture search. By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, an efficient family of object detection models is obtained.
The Fast-YOLOv4-SmallObj model is a modified version of Fast-YOLOv4 to improve the detection of small objects. Seven layers were added so that it predicts bounding boxes at 3 different scales instead of 2.
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TD-VAE, or Temporal Difference VAE, is a generative sequence model that learns representations containing explicit beliefs about states several steps into the future, and that can be rolled out directly without single-step transitions. TD-VAE is trained on pairs of temporally separated time points, using an analogue of temporal difference learning used in reinforcement learning.
Graph Convolutional Networks for Fake News Detection
Social media are nowadays one of the main news sources for millions of people around the globe due to their low cost, easy access and rapid dissemination. This however comes at the cost of dubious trustworthiness and significant risk of exposure to 'fake news', intentionally written to mislead the readers. Automatically detecting fake news poses challenges that defy existing content-based analysis approaches. One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing. Recent studies have shown that fake and real news spread differently on social media, forming propagation patterns that could be harnessed for the automatic fake news detection. Propagation-based approaches have multiple advantages compared to their content-based counterparts, among which is language independence and better resilience to adversarial attacks. In this paper we show a novel automatic fake news detection model based on geometric deep learning. The underlying core algorithms are a generalization of classical CNNs to graphs, allowing the fusion of heterogeneous data such as content, user profile and activity, social graph, and news propagation. Our model was trained and tested on news stories, verified by professional fact-checking organizations, that were spread on Twitter. Our experiments indicate that social network structure and propagation are important features allowing highly accurate (92.7% ROC AUC) fake news detection. Second, we observe that fake news can be reliably detected at an early stage, after just a few hours of propagation. Third, we test the aging of our model on training and testing data separated in time. Our results point to the promise of propagation-based approaches for fake news detection as an alternative or complementary strategy to content-based approaches.
Adaptive Second Order Optimization via Fisher Information
AdaFisher – an adaptive second-order optimizer that leverages a block-diagonal approximation to the Fisher information matrix for adaptive gradient preconditioning.
Concurrent Spatial and Channel Squeeze & Excitation (scSE)
Combines the channel attention of the widely known spatial squeeze and channel excitation (SE) block and the spatial attention of the channel squeeze and spatial excitation (sSE) block to build a spatial and channel attention mechanism for image segmentation tasks.
Spatial and Channel-wise Attention-based Convolutional Neural Network
As CNN features are naturally spatial, channel-wise and multi-layer, Chen et al. proposed a novel spatial and channel-wise attention-based convolutional neural network (SCA-CNN). It was designed for the task of image captioning, and uses an encoder-decoder framework where a CNN first encodes an input image into a vector and then an LSTM decodes the vector into a sequence of words. Given an input feature map and the previous time step LSTM hidden state , a spatial attention mechanism pays more attention to the semantically useful regions, guided by LSTM hidden state . The spatial attention model is: \begin{align} a(h{t-1}, X) &= \tanh(Conv1^{1 \times 1}(X) \oplus W1 h{t-1}) \end{align} \begin{align} \Phis(h{t-1}, X) &= \text{Softmax}(Conv2^{1 \times 1}(a(h{t-1}, X))) \end{align} where represents addition of a matrix and a vector. Similarly, channel-wise attention aggregates global information first, and then computes a channel-wise attention weight vector with the hidden state : \begin{align} b(h{t-1}, X) &= \tanh((W2\text{GAP}(X)+b2)\oplus W1h{t-1}) \end{align} \begin{align} \Phic(h{t-1}, X) &= \text{Softmax}(W3(b(h{t-1}, X))+b3) \end{align} Overall, the SCA mechanism can be written in one of two ways. If channel-wise attention is applied before spatial attention, we have \begin{align} Y &= f(X,\Phis(h{t-1}, X \Phic(h{t-1}, X)), \Phic(h{t-1}, X)) \end{align} and if spatial attention comes first: \begin{align} Y &= f(X,\Phis(h{t-1}, X), \Phic(h{t-1}, X \Phis(h{t-1}, X))) \end{align} where denotes the modulate function which takes the feature map and attention maps as input and then outputs the modulated feature map . Unlike previous attention mechanisms which consider each image region equally and use global spatial information to tell the network where to focus, SCA-Net leverages the semantic vector to produce the spatial attention map as well as the channel-wise attention weight vector. Being more than a powerful attention model, SCA-CNN also provides a better understanding of where and what the model should focus on during sentence generation.
Padé Activation Units
Parametrized learnable activation function, based on the Padé approximant.
Circular Dilated Convolutional Neural Networks
Point Gathering Network
PGNet is a point-gathering network for reading arbitrarily-shaped text in real-time. It is a single-shot text spotter, where the pixel-level character classification map is learned with proposed PG-CTC loss avoiding the usage of character-level annotations. With a PG-CTC decoder, we gather high-level character classification vectors from two-dimensional space and decode them into text symbols without NMS and RoI operations involved, which guarantees high efficiency. Additionally, reasoning the relations between each character and its neighbors, a graph refinement module (GRM) is proposed to optimize the coarse recognition and improve the end-to-end performance.