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 6301–6350 of 10580 papers

TitleStatusHype
Temporal knowledge graph representation learning with local and global evolutionsCode0
Conditional Independence Testing via Latent Representation LearningCode0
Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing LabelsCode0
A Novel Self-Knowledge Distillation Approach with Siamese Representation Learning for Action Recognition—0
Deep Stable Representation Learning on Electronic Health RecordsCode0
Temporal Contrastive Learning with Curriculum—0
A Class-Aware Representation Refinement Framework for Graph Classification—0
Vision-Language Adaptive Mutual Decoder for OOV-STR—0
Multi-Step Prediction in Linearized Latent State Spaces for Representation Learning—0
Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic ImagesCode0
Learning an Ensemble of Deep Fingerprint Representations—0
Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax—0
Models and Benchmarks for Representation Learning of Partially Observed SubgraphsCode0
Deep Multi-Scale Representation Learning with Attention for Automatic Modulation Classification—0
Binary Representation via Jointly Personalized Sparse HashingCode0
Identifying Weight-Variant Latent Causal Models—0
Representation Learning based and Interpretable Reactor System Diagnosis Using Denoising Padded Autoencoder—0
Dual Representation Learning for One-Step Clustering of Multi-View DataCode0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
Towards Disentangled Speech Representations—0
Unsupervised Representation Learning in Deep Reinforcement Learning: A ReviewCode0
Self-Supervised Face Presentation Attack Detection with Dynamic Grayscale Snippets—0
Pipeline-Invariant Representation Learning for Neuroimaging—0
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
MORI-RAN: Multi-view Robust Representation Learning via Hybrid Contrastive FusionCode0
Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning—0
Deep Hypergraph Structure Learning—0
Visual processing in context of reinforcement learning—0
A Survey on Temporal Graph Representation Learning and Generative Modeling—0
Spatio-Temporal Representation Learning Enhanced Source Cell-phone Recognition from Speech Recordings—0
MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image PretrainingCode0
Sustaining Fairness via Incremental LearningCode0
A Review of Knowledge Graph Completion—0
Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery—0
Spiral Contrastive Learning: An Efficient 3D Representation Learning Method for Unannotated CT Lesions—0
Neural PCA for Flow-Based Representation Learning—0
Multi-Modal Representation Learning with Self-Adaptive Threshold for Commodity VerificationCode0
Representation Learning of Knowledge Graph for Wireless Communication Networks—0
Meta-Causal Feature Learning for Out-of-Distribution Generalization—0
Prompt-Matched Semantic Segmentation—0
Self-Supervised Pretraining of Graph Neural Network for the Retrieval of Related Mathematical Expressions in Scientific Articles—0
Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders—0
Anatomy-Aware Contrastive Representation Learning for Fetal UltrasoundCode0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval—0
Representation Learning with Graph Neural Networks for Speech Emotion Recognition—0
SoMoFormer: Social-Aware Motion Transformer for Multi-Person Motion Prediction—0
Effective Transfer Learning for Low-Resource Natural Language Understanding—0
Disentangled Representation with Causal Constraints for Counterfactual Fairness—0
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation—0
Spectral Decomposition Representation for Reinforcement Learning—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