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 8151–8200 of 10580 papers

TitleStatusHype
GroupBERT: Enhanced Transformer Architecture with Efficient Grouped Structures—0
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection—0
3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning—0
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving—0
SAF: Smart Aggregation Framework for Revealing Atoms Importance Rank and Improving Prediction Rates in Drug Discovery—0
Graph Neural Networks Including Sparse Interpretability—0
Grounding-MD: Grounded Video-language Pre-training for Open-World Moment Detection—0
Saliency Guided Contrastive Learning on Scene Images—0
Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems—0
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning—0
SCRIPT: Self-Critic PreTraining of Transformers—0
GridMind: A Multi-Agent NLP Framework for Unified, Cross-Modal NFL Data Insights—0
Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification—0
SAM-Guided Robust Representation Learning for One-Shot 3D Medical Image Segmentation—0
Graph Neural Networks with Feature and Structure Aware Random Walk—0
Sample-efficient Adversarial Imitation Learning—0
Grid Jigsaw Representation with CLIP: A New Perspective on Image Clustering—0
GridCLIP: One-Stage Object Detection by Grid-Level CLIP Representation Learning—0
Sample-Specific Debiasing for Better Image-Text Models—0
Sample what you cant compress—0
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach—0
Variational Quantum Circuit Model for Knowledge Graphs Embedding—0
DEAAN: Disentangled Embedding and Adversarial Adaptation Network for Robust Speaker Representation Learning—0
Sampling Through the Lens of Sequential Decision Making—0
flexgrid2vec: Learning Efficient Visual Representations Vectors—0
De^2Gaze: Deformable and Decoupled Representation Learning for 3D Gaze Estimation—0
Green Learning: Introduction, Examples and Outlook—0
SA-Net: A deep spectral analysis network for image clustering—0
Adjusting Word Embeddings with Semantic Intensity Orders—0
Scoring and Classifying with Gated Auto-encoders—0
SCVRL: Shuffled Contrastive Video Representation Learning—0
Sat2Sound: A Unified Framework for Zero-Shot Soundscape Mapping—0
SATR-DL: Improving Surgical Skill Assessment and Task Recognition in Robot-assisted Surgery with Deep Neural Networks—0
AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assemblies—0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds—0
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis—0
GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements—0
SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data—0
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning—0
A Genetic Algorithm Approach for ImageRepresentation Learning through Color Quantization—0
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context—0
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules—0
SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields—0
Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning—0
Score-based Causal Representation Learning with Interventions—0
Multi-local Collaborative AutoEncoder—0
Graph U-Net—0
Anatomical Structure-Guided Medical Vision-Language Pre-training—0
Show:102550
← PrevPage 164 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