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
Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning—0
Efficient Deep Representation Learning by Adaptive Latent Space Sampling—0
Unsupervised Hierarchical Graph Representation Learning by Mutual Information MaximizationCode0
Spectral Graph Attention Network with Fast Eigen-approximation—0
Learning Shape Representations for Clothing Variations in Person Re-Identification—0
Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection—0
Active Perception and Representation for Robotic Manipulation—0
Text-based Person Search via Attribute-aided Matching—0
DAN: Dual-View Representation Learning for Adapting Stance Classifiers to New Domains—0
Micro-supervised Disturbance Learning: A Perspective of Representation Probability Distribution—0
Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations—0
PointINS: Point-based Instance Segmentation—0
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification—0
DHOG: Deep Hierarchical Object Grouping—0
Fairness by Learning Orthogonal Disentangled Representations—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity—0
Deep Inverse Feature Learning: A Representation Learning of Error—0
Inverse Feature Learning: Feature learning based on Representation Learning of Error—0
Adversarial Multimodal Representation Learning for Click-Through Rate Prediction—0
Noise Estimation Using Density Estimation for Self-Supervised Multimodal LearningCode0
Semi-Supervised StyleGAN for Disentanglement Learning—0
Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data—0
Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a VideoCode0
q-VAE for Disentangled Representation Learning and Latent Dynamical Systems—0
PushNet: Efficient and Adaptive Neural Message PassingCode0
Learning to Hash with Graph Neural Networks for Recommender Systems—0
Contrastive estimation reveals topic posterior information to linear models—0
Self-Supervised Graph Representation Learning via Global Context Prediction—0
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning—0
Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning—0
VAE/WGAN-Based Image Representation Learning For Pose-Preserving Seamless Identity Replacement In Facial Images—0
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees—0
Multi-Scale Neural network for EEG Representation Learning in BCI—0
Entity Profiling in Knowledge Graphs—0
Self-supervised Representation Learning for Ultrasound Video—0
Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation LearningCode0
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing—0
A Free-Energy Principle for Representation Learning—0
Acceleration of Actor-Critic Deep Reinforcement Learning for Visual Grasping in Clutter by State Representation Learning Based on Disentanglement of a Raw Input Image—0
Learning Representations by Predicting Bags of Visual WordsCode0
Evolving Losses for Unsupervised Video Representation Learning—0
Representation Learning Through Latent Canonicalizations—0
Provable Meta-Learning of Linear RepresentationsCode0
Towards Universal Representation Learning for Deep Face Recognition—0
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection—0
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective—0
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning—0
Dual Graph Representation Learning—0
Label-guided Learning for Text Classification—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