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 3901–3950 of 10580 papers

TitleStatusHype
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs—0
A Simple Framework for Open-Vocabulary Zero-Shot Segmentation—0
Diffusion Spectral Representation for Reinforcement Learning—0
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction—0
Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition—0
An Efficient NAS-based Approach for Handling Imbalanced Datasets—0
Latent Space Translation via Inverse Relative Projection—0
CLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned RepresentationCode0
Modeling of spatially embedded networks via regional spatial graph convolutional networksCode0
Geometric Self-Supervised Pretraining on 3D Protein Structures using Subgraphs—0
Latent Functional Maps: a spectral framework for representation alignment—0
Similarity-aware Syncretic Latent Diffusion Model for Medical Image Translation with Representation Learning—0
LARP: Language Audio Relational Pre-training for Cold-Start Playlist ContinuationCode0
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease—0
Learning telic-controllable state representations—0
Capturing Temporal Components for Time Series Classification—0
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning—0
Deblurring Neural Radiance Fields with Event-driven Bundle Adjustment—0
Towards Holistic Language-video Representation: the language model-enhanced MSR-Video to Text Dataset—0
RobGC: Towards Robust Graph Condensation—0
Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs—0
Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment—0
Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks—0
Towards Trustworthy Unsupervised Domain Adaptation: A Representation Learning Perspective for Enhancing Robustness, Discrimination, and Generalization—0
LFMamba: Light Field Image Super-Resolution with State Space Model—0
Semantic Graph Consistency: Going Beyond Patches for Regularizing Self-Supervised Vision Transformers—0
VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability EstimationCode0
Interventional Imbalanced Multi-Modal Representation Learning via β-Generalization Front-Door Criterion—0
Revisiting Spurious Correlation in Domain Generalization—0
Federated Face Forgery Detection Learning with Personalized RepresentationCode0
On GNN explanability with activation rules—0
Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding—0
A Scalable and Effective Alternative to Graph Transformers—0
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations—0
An Interpretable Alternative to Neural Representation Learning for Rating Prediction -- Transparent Latent Class Modeling of User ReviewsCode0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—0
On the Effectiveness of Supervision in Asymmetric Non-Contrastive LearningCode0
A Unified Graph Selective Prompt Learning for Graph Neural Networks—0
Fine-Grained Urban Flow Inference with Multi-scale Representation Learning—0
Neural Pose Representation Learning for Generating and Transferring Non-Rigid Object Poses—0
Vision Language Modeling of Content, Distortion and Appearance for Image Quality Assessment—0
Disentangled Hyperbolic Representation Learning for Heterogeneous Graphs—0
SSTFB: Leveraging self-supervised pretext learning and temporal self-attention with feature branching for real-time video polyp segmentation—0
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge—0
Cross-Modality Program Representation Learning for Electronic Design Automation with High-Level Synthesis—0
Self-supervised Graph Neural Network for Mechanical CAD Retrieval—0
Multiple Prior Representation Learning for Self-Supervised Monocular Depth Estimation via Hybrid TransformerCode0
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation—0
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding AnalysisCode0
Show:102550
← PrevPage 79 of 212Next →
All datasetsSciDocsAnimals-10CIFAR10Circle DataSports10

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