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 7801–7850 of 10580 papers

TitleStatusHype
Evolving Image Compositions for Feature Representation Learning—0
Watching Too Much Television is Good: Self-Supervised Audio-Visual Representation Learning from Movies and TV Shows—0
Directed Graph Embeddings in Pseudo-Riemannian Manifolds—0
On the Power of Multitask Representation Learning in Linear MDP—0
Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks—0
Speech Disorder Classification Using Extended Factorized Hierarchical Variational Auto-encoders—0
Which Mutual-Information Representation Learning Objectives are Sufficient for Control?—0
FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications—0
Node Classification Meets Link Prediction on Knowledge Graphs—0
Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings—0
Cross-Modal Attention Consistency for Video-Audio Unsupervised Learning—0
Representation Learning for Out-of-distribution Generalization in Reinforcement Learning—0
Exploration-Driven Representation Learning in Reinforcement Learning—0
Atlas Based Representation and Metric Learning on ManifoldsCode0
Learning Task-Relevant Representations with Selective Contrast for Reinforcement Learning in a Real-World Application—0
Density-Based Bonuses on Learned Representations for Reward-Free Exploration in Deep Reinforcement Learning—0
InfoBehavior: Self-supervised Representation Learning for Ultra-long Behavior Sequence via Hierarchical Grouping—0
Provable Adaptation across Multiway Domains via Representation Learning—0
Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation—0
Model Selection for Bayesian AutoencodersCode0
A Framework to Enhance Generalization of Deep Metric Learning methods using General Discriminative Feature Learning and Class Adversarial Neural NetworksCode0
Learning the Precise Feature for Cluster AssignmentCode0
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction—0
Robust Representation Learning via Perceptual Similarity Metrics—0
Cross-Modal Discrete Representation Learning—0
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition—0
MusicBERT: Symbolic Music Understanding with Large-Scale Pre-TrainingCode0
Linguistically Informed Masking for Representation Learning in the Patent DomainCode0
GroupBERT: Enhanced Transformer Architecture with Efficient Grouped Structures—0
Fairness-Aware Node Representation Learning—0
Multiple Kernel Representation Learning on NetworksCode0
DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching—0
Contrastive Representation Learning for Hand Shape Estimation—0
NWT: Towards natural audio-to-video generation with representation learningCode0
Multi-output Gaussian Processes for Uncertainty-aware Recommender SystemsCode0
SelfDoc: Self-Supervised Document Representation Learning—0
Shifting Transformation Learning for Out-of-Distribution Detection—0
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models—0
A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning—0
DisTop: Discovering a Topological representation to learn diverse and rewarding skills—0
Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning—0
Variational Leakage: The Role of Information Complexity in Privacy LeakageCode0
Local Disentanglement in Variational Auto-Encoders Using Jacobian L_1 RegularizationCode0
Lifelong Learning of Hate Speech Classification on Social Media—0
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RLCode0
Recursive Tree Attention: Improving Semantic Representations with Syntactic Tree Structured Attention Mechanism—0
Self-Supervised Learning of Domain Invariant Features for Depth Estimation—0
ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency—0
Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism—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