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 5201–5250 of 10580 papers

TitleStatusHype
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models—0
A Unified Graph Selective Prompt Learning for Graph Neural Networks—0
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion—0
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification—0
FedLog: Personalized Federated Classification with Less Communication and More Flexibility—0
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
A Unified Framework for Multi-distribution Density Ratio Estimation—0
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering—0
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems—0
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations—0
Federated Word2Vec: Leveraging Federated Learning to Encourage Collaborative Representation Learning—0
A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix—0
Federated Variational Learning for Anomaly Detection in Multivariate Time Series—0
Federated User Representation Learning—0
Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery—0
Federated Unsupervised Representation Learning—0
Federated Training of Dual Encoding Models on Small Non-IID Client Datasets—0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck—0
A Unified Framework for Adaptive Representation Enhancement and Inversed Learning in Cross-Domain Recommendation—0
Revisiting Catastrophic Forgetting in Class Incremental Learning—0
Federated Self-supervised Learning for Heterogeneous Clients—0
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning—0
Contrastive estimation reveals topic posterior information to linear models—0
Federated Representation Learning via Maximal Coding Rate Reduction—0
Contrastive Environmental Sound Representation Learning—0
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection—0
Federated Representation Learning for Automatic Speech Recognition—0
Federated Model Heterogeneous Matryoshka Representation Learning—0
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification—0
Contrastive Document Representation Learning with Graph Attention Networks—0
A Unified Collaborative Representation Learning for Neural-Network based Recommender Systems—0
Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement—0
Federated Graph Representation Learning using Self-Supervision—0
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching—0
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination—0
Contrastive Decoupled Representation Learning and Regularization for Speech-Preserving Facial Expression Manipulation—0
FedDAR: Federated Domain-Aware Representation Learning—0
Contrastive Data and Learning for Natural Language Processing—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
Self-supervised On-device Federated Learning from Unlabeled Streams—0
Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval—0
The Causal Structure of Domain Invariant Supervised Representation Learning—0
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features—0
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning—0
Feature Transformers: A Unified Representation Learning Framework for Lifelong Learning—0
Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition—0
Contrastive Continual Learning with Feature Propagation—0
Align Voting Behavior with Public Statements for Legislator Representation Learning—0
Adaptive Online Incremental Learning for Evolving Data Streams—0
Accurate Text-Enhanced Knowledge Graph Representation Learning—0
Show:102550
← PrevPage 105 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