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 5701–5750 of 10580 papers

TitleStatusHype
Generative Causal Representation Learning for Out-of-Distribution Motion ForecastingCode0
Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
PiRL: Participant-Invariant Representation Learning for Healthcare Using Maximum Mean Discrepancy and Triplet Loss—0
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks—0
Graph-Enhanced Emotion Neural DecodingCode0
Visible-Infrared Person Re-Identification via Patch-Mixed Cross-Modality Learning—0
Self-Organising Neural Discrete Representation Learning à la Kohonen—0
DKT-STDRL: Spatial and Temporal Representation Learning Enhanced Deep Knowledge Tracing for Learning Performance Prediction—0
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
Robust Representation Learning with Self-Distillation for Domain Generalization—0
Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems—0
Label-efficient Time Series Representation Learning: A Review—0
Exploiting Graph Structured Cross-Domain Representation for Multi-Domain RecommendationCode0
Efficient Fraud Detection Using Deep Boosting Decision TreesCode0
Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with DistractionsCode0
SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data—0
Cross-domain Random Pre-training with Prototypes for Reinforcement LearningCode0
A Survey on Spectral Graph Neural Networks—0
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks—0
Analyzing Multimodal Objectives Through the Lens of Generative Diffusion Guidance—0
EVC: Towards Real-Time Neural Image Compression with Mask Decay—0
Combining Reconstruction and Contrastive Methods for Multimodal Representations in RLCode0
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes—0
Trading Information between Latents in Hierarchical Variational AutoencodersCode0
Privacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes—0
Multi-task Representation Learning for Pure Exploration in Linear Bandits—0
Discovering interpretable Lagrangian of dynamical systems from data—0
CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data—0
Weakly-supervised Representation Learning for Video Alignment and Analysis—0
Multiview Representation Learning from Crowdsourced Triplet ComparisonsCode0
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion—0
Unsupervised Deep Learning for IoT Time Series—0
Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning—0
Heterophily-Aware Graph Attention Network—0
Spectral Augmentations for Graph Contrastive Learning—0
Audio Representation Learning by Distilling Video as Privileged Information—0
Self-supervised Geometric Features Discovery via Interpretable Attentio for Vehicle Re-Identification and Beyond (Complete Version)Code0
CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning—0
Rethinking Robust Contrastive Learning from the Adversarial PerspectiveCode0
JPEG Steganalysis Based on Steganographic Feature Enhancement and Graph Attention Learning—0
Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network—0
GRANDE: a neural model over directed multigraphs with application to anti-money laundering—0
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives—0
Reinforcement Learning in Low-Rank MDPs with Density Features—0
MOMA:Distill from Self-Supervised Teachers—0
Contrastive Learning with Consistent RepresentationsCode0
Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification—0
Aligning Robot and Human Representations—0
SPADE: Self-supervised Pretraining for Acoustic DisEntanglement—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