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 8901–8950 of 10580 papers

TitleStatusHype
Debiasing Diffusion Model: Enhancing Fairness through Latent Representation Learning in Stable Diffusion Model—0
Debiasing Graph Representation Learning based on Information Bottleneck—0
Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention—0
Deblurring Neural Radiance Fields with Event-driven Bundle Adjustment—0
PointGAT: A quantum chemical property prediction model integrating graph attention and 3D geometry—0
Decentralized Complete Dictionary Learning via ^4-Norm Maximization—0
Distributed Representations of Entities in Open-World Knowledge Graphs—0
PointINS: Point-based Instance Segmentation—0
Decoder-free Robustness Disentanglement without (Additional) Supervision—0
Decomposed Mutual Information Estimation for Contrastive Representation Learning—0
Decompose More and Aggregate Better: Two Closer Looks at Frequency Representation Learning for Human Motion Prediction—0
Decomposing Bilexical Dependencies into Semantic and Syntactic Vectors—0
Decomposing Mutual Information for Representation Learning—0
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network—0
PointJEM: Self-supervised Point Cloud Understanding for Reducing Feature Redundancy via Joint Entropy Maximization—0
DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes—0
Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning—0
Decorrelation-based Self-Supervised Visual Representation Learning for Writer Identification—0
Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation—0
Knowledge-aware Complementary Product Representation Learning—0
Decoupled Representation Learning for Skeleton-Based Gesture Recognition—0
Decoupled Self-supervised Learning for Non-Homophilous Graphs—0
ASR-enhanced Multimodal Representation Learning for Cross-Domain Product Retrieval—0
Decoupled Training for Long-Tailed Classification With Stochastic Representations—0
When MAML Can Adapt Fast and How to Assist When It Cannot—0
Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph—0
Decoupling feature propagation from the design of graph auto-encoders—0
PointMatch: A Consistency Training Framework for Weakly Supervised Semantic Segmentation of 3D Point Clouds—0
PointMoment:Mixed-Moment-based Self-Supervised Representation Learning for 3D Point Clouds—0
Combining Self-Supervised and Supervised Learning with Noisy Labels—0
PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds—0
DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach—0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning—0
Deep Adversarial Subspace Clustering—0
Deep Adversarial Transition Learning using Cross-Grafted Generative Stacks—0
DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors—0
Deep Anomaly Detection in Text—0
Points2Vec: Unsupervised Object-level Feature Learning from Point Clouds—0
Pointspectrum: Equivariance Meets Laplacian Filtering for Graph Representation Learning—0
Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning—0
Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood—0
Deep Audio-Visual Learning: A Survey—0
Deep Autoencoder-based Fuzzy C-Means for Topic Detection—0
Beyond the Kolmogorov Barrier: A Learnable Weighted Hybrid Autoencoder for Model Order Reduction—0
Deep auxiliary learning for visual localization using colorization task—0
Deep causal representation learning for unsupervised domain adaptation—0
DeepCell: Multiview Representation Learning for Post-Mapping Netlists—0
PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning—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