SOTAVerified

Representation Learning

Representation Learning is a process in machine learning where algorithms extract meaningful patterns from raw data to create representations that are easier to understand and process. These representations can be designed for interpretability, reveal hidden features, or be used for transfer learning. They are valuable across many fundamental machine learning tasks like image classification and retrieval.

Deep neural networks can be considered representation learning models that typically encode information which is projected into a different subspace. These representations are then usually passed on to a linear classifier to, for instance, train a classifier.

Representation learning can be divided into:

  • Supervised representation learning: learning representations on task A using annotated data and used to solve task B
  • Unsupervised representation learning: learning representations on a task in an unsupervised way (label-free data). These are then used to address downstream tasks and reducing the need for annotated data when learning news tasks. Powerful models like GPT and BERT leverage unsupervised representation learning to tackle language tasks.

More recently, self-supervised learning (SSL) is one of the main drivers behind unsupervised representation learning in fields like computer vision and NLP.

Here are some additional readings to go deeper on the task:

( Image credit: Visualizing and Understanding Convolutional Networks )

Papers

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Showing 4851–4900 of 10580 papers

TitleStatusHype
CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data—0
GEN Model: An Alternative Approach to Deep Neural Network Models—0
GENIUS: A Novel Solution for Subteam Replacement with Clustering-based Graph Neural Network—0
Critical Learning Periods in Deep Networks—0
Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies—0
Back to the Drawing Board for Fair Representation Learning—0
A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval—0
Generic Multimodal Spatially Graph Network for Spatially Embedded Network Representation Learning—0
Generic Multi-modal Representation Learning for Network Traffic Analysis—0
Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning—0
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation—0
Generative Slate Recommendation with Reinforcement Learning—0
Creating Your Editable 3D Photorealistic Avatar with Tetrahedron-constrained Gaussian Splatting—0
Creating small but meaningful representations of digital pathology images—0
FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks—0
Creating generalizable downstream graph models with random projections—0
Generative Pretraining for Paraphrase Evaluation—0
A Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embedding—0
Generative Pretraining for Paraphrase Evaluation—0
Generative or Contrastive? Phrase Reconstruction for Better Sentence Representation Learning—0
CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos—0
AXM-Net: Implicit Cross-Modal Feature Alignment for Person Re-identification—0
Amortized Variational Inference: A Systematic Review—0
Generative Modeling of Class Probability for Multi-Modal Representation Learning—0
Generative Modeling for Atmospheric Convection—0
Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm—0
Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories—0
Generative Adversarial Networks for Multimodal Representation Learning in Video Hyperlinking—0
COVID-19 Detection Based on Self-Supervised Transfer Learning Using Chest X-Ray Images—0
A Visual Analytics Framework for Contrastive Network Analysis—0
Amortized Inference Regularization—0
AdaVid: Adaptive Video-Language Pretraining—0
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images—0
CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation—0
Deep Representation Learning for Road Detection through Siamese Network—0
Generative Adversarial Image Synthesis with Decision Tree Latent Controller—0
Generating Videos with Scene Dynamics—0
Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE—0
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning—0
Generating Privacy-Preserving Process Data with Deep Generative Models—0
Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations—0
A Vision Check-up for Language Models—0
Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness—0
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models—0
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators—0
Generating Drug Repurposing Hypotheses through the Combination of Disease-Specific Hypergraphs—0
Coupled Representation Learning for Domains, Intents and Slots in Spoken Language Understanding—0
A Vision Centric Remote Sensing Benchmark—0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SciNCLAvg.81.8—Unverified
2SPECTERAvg.80—Unverified
3CiteomaticAvg.76—Unverified
4Sci-DeCLUTRAvg.66.6—Unverified
5SciBERTAvg.59.6—Unverified
6BioBERTAvg.58.8—Unverified
7CiteBERTAvg.58.8—Unverified
#ModelMetricClaimedVerifiedStatus
1top_model_weights_with_3d_21:1 Accuracy0.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet 18Accuracy (%)97.05—Unverified
#ModelMetricClaimedVerifiedStatus
1Morphological NetworkAccuracy97.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Max Margin ContrastiveSilhouette Score0.56—Unverified