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 8601–8650 of 10580 papers

TitleStatusHype
Context-Aware Multimodal Pretraining—0
Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network—0
Context-Aware Smoothing for Neural Machine Translation—0
Persistent Homology and Graphs Representation Learning—0
Context-Enhanced Multi-View Trajectory Representation Learning: Bridging the Gap through Self-Supervised Models—0
Optimizing Context-Enhanced Relational Joins—0
Context-invariant, multi-variate time series representations—0
Persistent Homology and Graphs Representation Learning—0
Self-Supervised Visual Representation Learning on Food Images—0
Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification—0
Contextual Gradient Flow Modeling for Large Language Model Generalization in Multi-Scale Feature Spaces—0
Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge—0
Contextual Knowledge Distillation for Transformer Compression—0
Contextual Representation Learning beyond Masked Language Modeling—0
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning—0
Self-supervised 3D Semantic Representation Learning for Vision-and-Language Navigation—0
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems—0
Contextures: Representations from Contexts—0
Contextures: The Mechanism of Representation Learning—0
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs—0
Continual Causal Inference with Incremental Observational Data—0
Continual Contrastive Finetuning Improves Low-Resource Relation Extraction—0
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity—0
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation—0
Continual Learning for Motion Prediction Model via Meta-Representation Learning and Optimal Memory Buffer Retention Strategy—0
Continual Learning of Nonlinear Independent Representations—0
Continual Lifelong Causal Effect Inference with Real World Evidence—0
Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer—0
Personalized PageRank meets Graph Attention Networks—0
Continual State Representation Learning for Reinforcement Learning using Generative Replay—0
Continual Unsupervised Representation Learning—0
Continual Vision-Language Representation Learning with Off-Diagonal Information—0
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning—0
Continuous Adversarial Text Representation Learning for Affective Recognition—0
Continuous Histogram Loss: Beyond Neural Similarity—0
Continuous Tensor Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems—0
Language Model Mapping in Multimodal Music Learning: A Grand Challenge Proposal—0
Continuous-time Graph Representation with Sequential Survival Process—0
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration—0
Personalizing Pre-trained Models—0
Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning—0
Self-Supervised Visual Representations Learning by Contrastive Mask Prediction—0
Person search: New paradigm of person re-identification: A survey and outlook of recent works—0
Gaze Prediction as a Function of Eye Movement Type and Individual Differences—0
Contrastive Approach to Prior Free Positive Unlabeled Learning—0
Contrastive Attention Maps for Self-Supervised Co-Localization—0
Learning Video Representations using Contrastive Bidirectional Transformer—0
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
Contrastive Classification and Representation Learning with Probabilistic Interpretation—0
Show:102550
← PrevPage 173 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