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 8651–8700 of 10580 papers

TitleStatusHype
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning—0
Perturbation Ontology based Graph Attention Networks—0
Contrastive Continual Learning with Feature Propagation—0
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage—0
Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval—0
Contrastive Data and Learning for Natural Language Processing—0
Contrastive Decoupled Representation Learning and Regularization for Speech-Preserving Facial Expression Manipulation—0
PGAHum: Prior-Guided Geometry and Appearance Learning for High-Fidelity Animatable Human Reconstruction—0
Contrastive Document Representation Learning with Graph Attention Networks—0
Contrastive Environmental Sound Representation Learning—0
Contrastive estimation reveals topic posterior information to linear models—0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck—0
Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery—0
PGX: A Multi-level GNN Explanation Framework Based on Separate Knowledge Distillation Processes—0
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification—0
PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction—0
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems—0
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight—0
Contrastive Learning for Low Resource Machine Translation—0
Contrastive Learning for Regression on Hyperspectral Data—0
Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Data Classification—0
Contrastive learning, multi-view redundancy, and linear models—0
Phase Matching for Out-of-Distribution Generalization—0
Phase transitions and sample complexity in Bayes-optimal matrix factorization—0
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency—0
Contrastive Learning of Person-independent Representations for Facial Action Unit Detection—0
Contrastive Learning on Multimodal Analysis of Electronic Health Records—0
Contrastive Learning Through Time—0
Phase Transitions for the Information Bottleneck in Representation Learning—0
PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery—0
Contrastive Learning with Nasty Noise—0
Contrastive Learning with Negative Sampling Correction—0
Phonetic-assisted Multi-Target Units Modeling for Improving Conformer-Transducer ASR system—0
Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods—0
PH-VAE: A Polynomial Hierarchical Variational Autoencoder Towards Disentangled Representation Learning—0
Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification—0
Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks—0
Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification—0
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis—0
Contrastive Multi-Task Dense Prediction—0
Contrastive Multiview Coding with Electro-optics for SAR Semantic Segmentation—0
Self-supervised Activity Representation Learning with Incremental Data: An Empirical Study—0
Contrastive Positive Mining for Unsupervised 3D Action Representation Learning—0
Physics-Driven Local-Whole Elastic Deformation Modeling for Point Cloud Representation Learning—0
Contrastive Predictive Coding for Anomaly Detection—0
Contrastive Pre-training for Imbalanced Corporate Credit Ratings—0
Contrastive Rendering for Ultrasound Image Segmentation—0
Contrastive Representation Learning: A Framework and Review—0
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs—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