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 6051–6100 of 10580 papers

TitleStatusHype
Behavior Prior Representation learning for Offline Reinforcement LearningCode0
RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild—0
FUNCK: Information Funnels and Bottlenecks for Invariant Representation Learning—0
MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid—0
Fast Adaptive Federated Bilevel Optimization—0
SyncTalkFace: Talking Face Generation with Precise Lip-Syncing via Audio-Lip Memory—0
Joint Data and Feature Augmentation for Self-Supervised Representation Learning on Point Clouds—0
On the Informativeness of Supervision Signals—0
Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided ApproachCode0
Deep Multimodal Fusion for Generalizable Person Re-identificationCode0
Augmentation Invariant Manifold Learning—0
Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound—0
Disentangled representation learning for multilingual speaker recognition—0
Invariant and consistent: Unsupervised representation learning for few-shot visual recognition—0
Improving Variational Autoencoders with Density Gap-based RegularizationCode0
Higher-order mutual information reveals synergistic sub-networks for multi-neuron importance—0
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—0
The Numerical Stability of Hyperbolic Representation LearningCode0
Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce dataCode0
Disentangled (Un)Controllable FeaturesCode0
FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender SystemsCode0
DyG2Vec: Efficient Representation Learning for Dynamic GraphsCode0
Representation Learning for General-sum Low-rank Markov Games—0
Pair DETR: Contrastive Learning Speeds Up DETR Training—0
Rare Wildlife Recognition with Self-Supervised Representation LearningCode0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
Spectral Representation Learning for Conditional Moment Models—0
Articulatory Representation Learning Via Joint Factor Analysis and Neural Matrix Factorization—0
Application of Knowledge Distillation to Multi-task Speech Representation Learning—0
Generalized Laplacian Positional Encoding for Graph Representation Learning—0
Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer—0
A Survey on Causal Representation Learning and Future Work for Medical Image AnalysisCode0
LegoNet: A Fast and Exact Unlearning Architecture—0
Domain Generalization through the Lens of Angular InvarianceCode0
When does mixup promote local linearity in learned representations?—0
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis—0
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR PredictionCode0
Federated Graph Representation Learning using Self-Supervision—0
MAEEG: Masked Auto-encoder for EEG Representation Learning—0
Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision—0
Disentangled and Robust Representation Learning for Bragging Classification in Social Media—0
Implications of sparsity and high triangle density for graph representation learning—0
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis—0
End-to-End Multimodal Representation Learning for Video Dialog—0
Efficient Utilization of Large Pre-Trained Models for Low Resource ASR—0
Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness—0
Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning—0
Auxiliary task discovery through generate-and-test—0
Disentangled Speech Representation Learning for One-Shot Cross-lingual Voice Conversion Using β-VAE—0
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering—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