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 3526–3550 of 10580 papers

TitleStatusHype
EmotionX-JTML: Detecting emotions with Attention—0
Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
FedDAR: Federated Domain-Aware Representation Learning—0
Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies—0
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination—0
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching—0
Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning—0
Federated Graph Representation Learning using Self-Supervision—0
A Survey on Spectral Graph Neural Networks—0
Graph Representation Learning for Interactive Biomolecule Systems—0
Federated Model Heterogeneous Matryoshka Representation Learning—0
Federated Representation Learning for Automatic Speech Recognition—0
Contrastive Environmental Sound Representation Learning—0
Federated Representation Learning via Maximal Coding Rate Reduction—0
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning—0
Federated Self-supervised Learning for Heterogeneous Clients—0
Federated Training of Dual Encoding Models on Small Non-IID Client Datasets—0
Federated Unsupervised Representation Learning—0
Federated User Representation Learning—0
Federated Variational Learning for Anomaly Detection in Multivariate Time Series—0
Emotion Dynamics Modeling via BERT—0
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations—0
Emotion-Aware Speech Self-Supervised Representation Learning with Intensity Knowledge—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—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