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 8201–8250 of 10580 papers

TitleStatusHype
DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition—0
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation—0
DAViD: Domain Adaptive Visually-Rich Document Understanding with Synthetic Insights—0
Self-Distilled Representation Learning for Time Series—0
Selective Cross-Domain Consistency Regularization for Time Series Domain Generalization—0
SeisRDT: Latent Diffusion Model Based On Representation Learning For Seismic Data Interpolation And Reconstruction—0
BEVPose: Unveiling Scene Semantics through Pose-Guided Multi-Modal BEV Alignment—0
Selective Cascade of Residual ExtraTrees—0
Graph Spring Neural ODEs for Link Sign Prediction—0
BEV-Guided Multi-Modality Fusion for Driving Perception—0
Graph Self-Contrast Representation Learning—0
GraphScale: A Framework to Enable Machine Learning over Billion-node Graphs—0
Between-Sample Relationship in Learning Tabular Data Using Graph and Attention Networks—0
Graph sampling for node embedding—0
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification—0
Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning—0
Distance-Preserving Spatial Representations in Genomic Data—0
Graph Representation Learning with Diffusion Generative Models—0
Graph Representation Learning with Individualization and Refinement—0
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification—0
Graph Representation Learning via Multi-task Knowledge Distillation—0
Data-Driven Offline Decision-Making via Invariant Representation Learning—0
A Distributed Deep Representation Learning Model for Big Image Data Classification—0
Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models—0
Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity—0
SelfDoc: Self-Supervised Document Representation Learning—0
Data Dimension Reduction makes ML Algorithms efficient—0
SE-GCL: An Event-Based Simple and Effective Graph Contrastive Learning for Text Representation—0
Graph Representation Learning via Contrasting Cluster Assignments—0
Graph Representation Learning Towards Patents Network Analysis—0
SCRIPT: Self-Critic PreTraining of Transformers—0
Beta-VAE has 2 Behaviors: PCA or ICA?—0
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease—0
SCoRe: Submodular Combinatorial Representation Learning—0
ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function—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
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
Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learning—0
Scoring and Classifying with Gated Auto-encoders—0
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning—0
SEGEN: SAMPLE-ENSEMBLE GENETIC EVOLUTIONARY NETWORK MODEL—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
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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