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 9451–9500 of 10580 papers

TitleStatusHype
Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers—0
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks—0
Privacy-preserving Voice Analysis via Disentangled Representations—0
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning—0
Dropout Training for SVMs with Data Augmentation—0
Dropping Convexity for More Efficient and Scalable Online Multiview Learning—0
Privacy Safe Representation Learning via Frequency Filtering Encoder—0
Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent Representations—0
DSVAE: Interpretable Disentangled Representation for Synthetic Speech Detection—0
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning—0
DTN: Deep Multiple Task-specific Feature Interactions Network for Multi-Task Recommendation—0
Privileged Zero-Shot AutoML—0
Dual-Channel Multiplex Graph Neural Networks for Recommendation—0
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior—0
Dual Contradistinctive Generative Autoencoder—0
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models—0
Dual Contrastive Learning for Spatio-temporal Representation—0
MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction—0
Self-Supervised Graph Representation Learning via Global Context Prediction—0
Probabilistic Latent Variable Modeling for Dynamic Friction Identification and Estimation—0
A Cyclically-Trained Adversarial Network for Invariant Representation Learning—0
Dual Encoder-Decoder based Generative Adversarial Networks for Disentangled Facial Representation Learning—0
Probabilistic Lexical Manifold Construction in Large Language Models via Hierarchical Vector Field Interpolation—0
Dual-Granularity Contrastive Learning for Session-based Recommendation—0
Probabilistic Multimodal Representation Learning—0
Dual Graph Complementary Network—0
Dual Graph Representation Learning—0
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness—0
Probabilistic Representations for Video Contrastive Learning—0
Probabilistic World Modeling with Asymmetric Distance Measure—0
Dynamic Traceback Learning for Medical Report Generation—0
Dual-Modality Representation Learning for Molecular Property Prediction—0
Dual Motion GAN for Future-Flow Embedded Video Prediction—0
Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis—0
Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning—0
Dual Space Graph Contrastive Learning—0
Dual-space Hierarchical Learning for Goal-guided Conversational Recommendation—0
Probing Contextual Language Models for Common Ground with Visual Representations—0
Dual Transformer for Point Cloud Analysis—0
Probing the Robustness of Independent Mechanism Analysis for Representation Learning—0
AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering—0
Duplex: Dual Prototype Learning for Compositional Zero-Shot Learning—0
DuRep: Dual-Mode Speech Representation Learning via ASR-Aware Distillation—0
Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning—0
DYAN: A Dynamical Atoms-Based Network for Video Prediction—0
DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models—0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstruction—0
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning—0
Procedural Generalization by Planning with Self-Supervised World 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