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 8251–8300 of 10580 papers

TitleStatusHype
ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function—0
Linking-Enhanced Pre-Training for Table Semantic Parsing—0
Graph Representation Learning on Tissue-Specific Multi-Omics—0
Data Considerations in Graph Representation Learning for Supply Chain Networks—0
Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows—0
Searching for Alignment in Face Recognition—0
Graph Representation Learning via Multi-task Knowledge Distillation—0
A Distributed Deep Representation Learning Model for Big Image Data Classification—0
BERT vs ALBERT explained—0
Neuradicon: operational representation learning of neuroimaging reports—0
Score-based Causal Representation Learning with Interventions—0
SCLIFD:Supervised Contrastive Knowledge Distillation for Incremental Fault Diagnosis under Limited Fault Data—0
Graph representation learning for street networks—0
Securing Social Media Against Deepfakes using Identity, Behavioral, and Geometric Signatures—0
Security and Privacy Enhanced Gait Authentication with Random Representation Learning and Digital Lockers—0
SEDD-PCC: A Single Encoder-Dual Decoder Framework For End-To-End Learned Point Cloud Compression—0
Self-Paced Imbalance Rectification for Class Incremental Learning—0
SEED: A Structural Encoder for Embedding-Driven Decoding in Time Series Prediction with LLMs—0
Graph Representation Learning for Spatial Image Steganalysis—0
Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning—0
BERT-MK: Integrating Graph Contextualized Knowledge into Pre-trained Language Models—0
A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024—0
Seeing the Whole in the Parts in Self-Supervised Representation Learning—0
Graph Self-Contrast Representation Learning—0
Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learning—0
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning—0
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields—0
SceneRec: Scene-Based Graph Neural Networks for Recommender Systems—0
Scene Graph Contrastive Learning for Embodied Navigation—0
Graph Representation Learning for Popularity Prediction Problem: A Survey—0
Segment Any Building—0
Scene-Aware Feature Matching—0
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing—0
SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs—0
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data—0
Graph Representation Learning for Interactive Biomolecule Systems—0
BERT Meets Relational DB: Contextual Representations of Relational Databases—0
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization—0
Graph Representation Learning for Infrared and Visible Image Fusion—0
SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption—0
Selective Cross-Domain Consistency Regularization for Time Series Domain Generalization—0
Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain—0
Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints—0
A Differential Topological View of Challenges in Learning with Feedforward Neural Networks—0
Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity—0
Self-MI: Efficient Multimodal Fusion via Self-Supervised Multi-Task Learning with Auxiliary Mutual Information Maximization—0
Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study—0
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta—0
Graph Representation learning for Audio & Music genre Classification—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
6CiteBERTAvg.58.8—Unverified
7BioBERTAvg.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