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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9301–9350 of 10580 papers

TitleStatusHype
Disentangled Representation Learning with Wasserstein Total Correlation—0
Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs—0
Disentangled Representation Learning with Transmitted Information Bottleneck—0
Disentangled Representations in Neural Models—0
Disentangled Representation with Causal Constraints for Counterfactual Fairness—0
Disentangled Representation with Dual-stage Feature Learning for Face Anti-spoofing—0
Disentangled Speaker Representation Learning via Mutual Information Minimization—0
Disentangled Speech Representation Learning Based on Factorized Hierarchical Variational Autoencoder with Self-Supervised Objective—0
Disentangled Speech Representation Learning for One-Shot Cross-lingual Voice Conversion Using β-VAE—0
Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness—0
Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model—0
Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning—0
Pre-training, Fine-tuning and Re-ranking: A Three-Stage Framework for Legal Question Answering—0
Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences—0
Implicit Causal Representation Learning via Switchable Mechanisms—0
Disentanglement of Correlated Factors via Hausdorff Factorized Support—0
Disentangling Age and Identity with a Mutual Information Minimization Approach for Cross-Age Speaker Verification—0
Disentangling by Partitioning: A Representation Learning Framework for Multimodal Sensory Data—0
Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models—0
Disentangling Factors of Variations Using Few Labels—0
Disentangling Factors of Variation Using Few Labels—0
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models—0
Disentangling Patterns and Transformations from One Sequence of Images with Shape-invariant Lie Group Transformer—0
Disentangling Properties of Contrastive Methods—0
Disentangling Singlish Discourse Particles with Task-Driven Representation—0
DisProtEdit: Exploring Disentangled Representations for Multi-Attribute Protein Editing—0
Adapted-MoE: Mixture of Experts with Test-Time Adaption for Anomaly Detection—0
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning—0
Pre-training Graph Neural Network for Cross Domain Recommendation—0
Distillation Using Oracle Queries for Transformer-Based Human-Object Interaction Detection—0
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning—0
E^3NeRF: Efficient Event-Enhanced Neural Radiance Fields from Blurry Images—0
Pretraining Reward-Free Representations for Data-Efficient Reinforcement Learning—0
Distilling Localization for Self-Supervised Representation Learning—0
Pre-Training Transformers as Energy-Based Cloze Models—0
Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval—0
Distinctive Feature Codec: Adaptive Segmentation for Efficient Speech Representation—0
DisTop: Discovering a Topological representation to learn diverse and rewarding skills—0
Distortion-Disentangled Contrastive Learning—0
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction—0
Distributed Decision Trees—0
Pre-training Universal Language Representation—0
Distributed representation of patients and its use for medical cost prediction—0
Distributed Representations for Compositional Semantics—0
Distributed Representations of Geographically Situated Language—0
Decentralized Unsupervised Learning of Visual Representations—0
Distributed Variational Representation Learning—0
Distributed Word Representation Learning for Cross-Lingual Dependency Parsing—0
Distributional Decision Transformer for Hindsight Information Matching—0
Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations—0
Show:102550
← PrevPage 187 of 212Next →
All datasetsSciDocsAnimals-10CIFAR10Circle DataSports10

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