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 2851–2900 of 10580 papers

TitleStatusHype
Auto-Encoder based Co-Training Multi-View Representation Learning—0
Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification—0
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks—0
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination—0
Adaptive Region Pooling for Fine-Grained Representation Learning—0
Contrastive Learning with Negative Sampling Correction—0
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching—0
Contrastive Learning with Nasty Noise—0
All the attention you need: Global-local, spatial-channel attention for image retrieval—0
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives—0
All-optical graph representation learning using integrated diffractive photonic computing units—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
Contrastive Learning Through Time—0
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning—0
Contrastive Learning on Multimodal Analysis of Electronic Health Records—0
A Uniform Representation Learning Method for OCT-based Fingerprint Presentation Attack Detection and Reconstruction—0
Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification—0
FedDAR: Federated Domain-Aware Representation Learning—0
Federated Graph Representation Learning using Self-Supervision—0
Federated Training of Dual Encoding Models on Small Non-IID Client Datasets—0
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning—0
Fill the Gap: Quantifying and Reducing the Modality Gap in Image-Text Representation Learning—0
Weakly Supervised LiDAR Semantic Segmentation via Scatter Image Annotation—0
Contrastive Learning of Person-independent Representations for Facial Action Unit Detection—0
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency—0
A Unified View on Neural Message Passing with Opinion Dynamics for Social Networks—0
Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation—0
All-in-One Transferring Image Compression from Human Perception to Multi-Machine Perception—0
Contrastive learning, multi-view redundancy, and linear models—0
A Unified Transformer-based Network for multimodal Emotion Recognition—0
A Unified Representation Learning Strategy for Open Relation Extraction with Ranked List Loss—0
Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems—0
Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition—0
Contrastive Learning for Regression on Hyperspectral Data—0
Contrastive Learning for Low Resource Machine Translation—0
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight—0
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems—0
UIILD: A Unified Interpretable Intelligent Learning Diagnosis Framework for Intelligent Tutoring Systems—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
A Unified Graph Selective Prompt Learning for Graph Neural Networks—0
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering—0
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification—0
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
A Unified Framework for Multi-distribution Density Ratio Estimation—0
Accurate Text-Enhanced Knowledge Graph Representation Learning—0
Self-supervised audio representation learning for mobile devices—0
Feature Transformers: A Unified Representation Learning Framework for Lifelong Learning—0
A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix—0
Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery—0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck—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