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

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Showing 3951–4000 of 10580 papers

TitleStatusHype
Hypergraph Node Representation Learning with One-Stage Message Passing—0
Hypergraph Pre-training with Graph Neural Networks—0
Code Representation Learning At Scale—0
Cross Modal Global Local Representation Learning from Radiology Reports and X-Ray Chest Images—0
Efficient Receptive Field Learning by Dynamic Gaussian Structure—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
Efficient Planning with Latent Diffusion—0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval—0
Cross-modal Representation Learning for Zero-shot Action Recognition—0
GRADE: Graph Dynamic Embedding—0
Efficient Object-centric Representation Learning with Pre-trained Geometric Prior—0
Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation—0
Gradients as Features for Deep Representation Learning—0
Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection—0
AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning—0
Efficient Multiscale Multimodal Bottleneck Transformer for Audio-Video Classification—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
Cross-Patient Pseudo Bags Generation and Curriculum Contrastive Learning for Imbalanced Multiclassification of Whole Slide Image—0
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training—0
Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design—0
Efficient Multi-Model Fusion with Adversarial Complementary Representation Learning—0
Learning from Neighbors: Category Extrapolation for Long-Tail Learning—0
Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs—0
Efficient Model-Free Exploration in Low-Rank MDPs—0
Graph2Tac: Online Representation Learning of Formal Math Concepts—0
A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond—0
Efficient Message Passing Architecture for GCN Training on HBM-based FPGAs with Orthogonal Topology On-Chip Networks—0
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark—0
Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework—0
Graph AI in Medicine—0
Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition—0
Graph Anomaly Detection via Adaptive Test-time Representation Learning across Out-of-Distribution Domains—0
Cross-subject Action Unit Detection with Meta Learning and Transformer-based Relation Modeling—0
Graph Attention Collaborative Similarity Embedding for Recommender System—0
Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection—0
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification—0
Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks—0
Graph-based Aspect Representation Learning for Entity Resolution—0
Graph-Based Generative Representation Learning of Semantically and Behaviorally Augmented Floorplans—0
Cross-Task Representation Learning for Anatomical Landmark Detection—0
Graph-based Isometry Invariant Representation Learning—0
AdaF^2M^2: Comprehensive Learning and Responsive Leveraging Features in Recommendation System—0
CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning—0
Efficient Learning of Domain-invariant Image Representations—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