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 2551–2600 of 10580 papers

TitleStatusHype
CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection—0
BEAR: A Video Dataset For Fine-grained Behaviors Recognition Oriented with Action and Environment Factors—0
Analysis of Augmentations for Contrastive ECG Representation Learning—0
CSE-SFP: Enabling Unsupervised Sentence Representation Learning via a Single Forward Pass—0
BEAN: Interpretable Representation Learning with Biologically-Enhanced Artificial Neuronal Assembly Regularization—0
ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning—0
CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning—0
BDetCLIP: Multimodal Prompting Contrastive Test-Time Backdoor Detection—0
A Deep Paradigm for Articulatory Speech Representation Learning via Neural Convolutive Sparse Matrix Factorization—0
Crowd Counting with Deep Structured Scale Integration Network—0
Analysing Fairness of Privacy-Utility Mobility Models—0
Estimating Conditional Average Treatment Effects via Sufficient Representation Learning—0
Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering—0
Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification—0
BCDR: Betweenness Centrality-based Distance Resampling for Graph Shortest Distance Embedding—0
Cross view link prediction by learning noise-resilient representation consensus—0
An algebraic theory to discriminate qualia in the brain—0
Guiding Graph Embeddings using Path-Ranking Methods for Error Detection innoisy Knowledge Graphs—0
Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data—0
CrossVideoMAE: Contrastive Spatiotemporal and Semantic Representation Learning from Videos and Images with Masked Autoencoders—0
Deep Representation Learning for Road Detection through Siamese Network—0
Error Analysis on Graph Laplacian Regularized Estimator—0
ERSOM: A Structural Ontology Matching Approach Using Automatically Learned Entity Representation—0
Estimating Galactic Distances From Images Using Self-supervised Representation Learning—0
Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning—0
Cross-Task Representation Learning for Anatomical Landmark Detection—0
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning—0
Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks—0
Bayesian representation learning with oracle constraints—0
A Closer Look at Personalization in Federated Image Classification—0
Cross-subject Action Unit Detection with Meta Learning and Transformer-based Relation Modeling—0
Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition—0
Bayesian Neural Decoding Using A Diversity-Encouraging Latent Representation Learning Method—0
Cross-State Self-Constraint for Feature Generalization in Deep Reinforcement Learning—0
An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Model—0
3D Graph Contrastive Learning for Molecular Property Prediction—0
ERL-Net: Entangled Representation Learning for Single Image De-Raining—0
Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation—0
Bayesian Models of Functional Connectomics and Behavior—0
Bayesian Learning of Latent Representations of Language Structures—0
A Mutually Reinforced Framework for Pretrained Sentence Embeddings—0
Cross-Patient Pseudo Bags Generation and Curriculum Contrastive Learning for Imbalanced Multiclassification of Whole Slide Image—0
Cross-Paced Representation Learning with Partial Curricula for Sketch-based Image Retrieval—0
A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation—0
A Deep Latent Space Model for Directed Graph Representation Learning—0
Bayesian Hierarchical Words Representation Learning—0
Equivariant Representation Learning for Symmetry-Aware Inference with Guarantees—0
Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection—0
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression—0
Evaluating Self-Supervised Speech Representations for Indigenous American Languages—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