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 10251–10300 of 10580 papers

TitleStatusHype
Reconstruction for disentanglement, Contrast for invariance—0
GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification—0
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
Reconstruction for Powerful Graph Representations—0
Effective Latent Differential Equation Models via Attention and Multiple Shooting—0
Reconstruction of Hidden Representation for Robust Feature Extraction—0
ReConTab: Regularized Contrastive Representation Learning for Tabular Data—0
GPS: A Policy-driven Sampling Approach for Graph Representation Learning—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
GPU Activity Prediction using Representation Learning—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
GRADE: Graph Dynamic Embedding—0
Gradients as Features for Deep Representation Learning—0
Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection—0
Gradual Learning of Matrix-Space Models of Language for Sentiment Analysis—0
Graffe: Graph Representation Learning via Diffusion Probabilistic Models—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
GraFT: Gradual Fusion Transformer for Multimodal Re-Identification—0
GraLSP: Graph Neural Networks with Local Structural Patterns—0
GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation—0
A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges—0
GRANDE: a neural model over directed multigraphs with application to anti-money laundering—0
Granular-ball Representation Learning for Deep CNN on Learning with Label Noise—0
Learning from Neighbors: Category Extrapolation for Long-Tail Learning—0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype—0
Graph2Tac: Online Representation Learning of Formal Math Concepts—0
RECS: Robust Graph Embedding Using Connection Subgraphs—0
A Comprehensive Survey on Cross-modal Retrieval—0
RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems—0
Graph AI in Medicine—0
Graph Anomaly Detection via Adaptive Test-time Representation Learning across Out-of-Distribution Domains—0
Graph Attention Collaborative Similarity Embedding for Recommender System—0
Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models—0
Graph-based Aspect Representation Learning for Entity Resolution—0
Graph-Based Generative Representation Learning of Semantically and Behaviorally Augmented Floorplans—0
Graph-based Isometry Invariant Representation Learning—0
Semantic Information Extraction for Improved Word Embeddings—0
Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks—0
Recurrent Exploration Networks for Recommender Systems—0
Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities—0
Graph Anomaly Detection in Time Series: A Survey—0
Graph-based Unsupervised Disentangled Representation Learning via Multimodal Large Language Models—0
GraphBit: Bitwise Interaction Mining via Deep Reinforcement Learning—0
GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Graph Condensation for Inductive Node Representation Learning—0
Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining—0
Recurrent Network Models for Human Dynamics—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