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 4751–4800 of 10580 papers

TitleStatusHype
Understanding Deep Representation Learning via Layerwise Feature Compression and DiscriminationCode0
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised LearningCode0
Low-Rank MDPs with Continuous Action Spaces—0
HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification—0
Augment the Pairs: Semantics-Preserving Image-Caption Pair Augmentation for Grounding-Based Vision and Language ModelsCode0
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
Identifying Linearly-Mixed Causal Representations from Multi-Node InterventionsCode0
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis—0
Learning Disentangled Speech Representations—0
ProS: Facial Omni-Representation Learning via Prototype-based Self-Distillation—0
Disentangled Representation Learning with Transmitted Information Bottleneck—0
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal GraphsCode0
Research Team Identification Based on Representation Learning of Academic Heterogeneous Information Network—0
Look-Ahead Selective Plasticity for Continual Learning of Visual TasksCode0
Recommendations by Concise User Profiles from Review Text—0
Joint Learning of Local and Global Features for Aspect-based Sentiment Classification—0
CenterRadarNet: Joint 3D Object Detection and Tracking Framework using 4D FMCW Radar—0
Causal Structure Representation Learning of Confounders in Latent Space for RecommendationCode0
MIST: Defending Against Membership Inference Attacks Through Membership-Invariant Subspace Training—0
Bayes-enhanced Multi-view Attention Networks for Robust POI Recommendation—0
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
Multi-task Representation Learning for Pure Exploration in Bilinear Bandits—0
Semantic Representation Learning of Scientific Literature based on Adaptive Feature and Graph Neural Network—0
Language Model Training Paradigms for Clinical Feature EmbeddingsCode0
Graph Representation Learning for Infrared and Visible Image Fusion—0
Privacy-preserving design of graph neural networks with applications to vertical federated learning—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving—0
Generalized Category Discovery with Clustering Assignment Consistency—0
Towards Practical Non-Adversarial Distribution Matching—0
KeyGen2Vec: Learning Document Embedding via Multi-label Keyword Generation in Question-Answering—0
A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data & Overparameterization?—0
Transmission line condition prediction based on semi-supervised learning—0
On Linear Separation Capacity of Self-Supervised Representation Learning—0
Object-centric architectures enable efficient causal representation learningCode0
Adversarial Bootstrapped Question Representation Learning for Knowledge TracingCode0
Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling—0
Patch-Wise Self-Supervised Visual Representation Learning: A Fine-Grained ApproachCode0
ReConTab: Regularized Contrastive Representation Learning for Tabular Data—0
SSL Framework for Causal Inconsistency between Structures and Representations—0
Feature Selection in the Contrastive Analysis SettingCode0
State-Action Similarity-Based Representations for Off-Policy EvaluationCode0
Causal disentanglement of multimodal data—0
Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs—0
Shape-centered Representation Learning for Visible-Infrared Person Re-identification—0
Unsupervised Representation Learning for Diverse Deformable Shape Collections—0
Grid Jigsaw Representation with CLIP: A New Perspective on Image Clustering—0
Relational Object-Centric Actor-Critic—0
Privacy-preserving Representation Learning for Speech Understanding—0
Unleashing the potential of GNNs via Bi-directional Knowledge Transfer—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