SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 2701–2750 of 6661 papers

TitleStatusHype
Multi-perspective Contrastive Logit Distillation—0
DR-BFR: Degradation Representation with Diffusion Models for Blind Face Restoration—0
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity—0
Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications—0
Masked Image Contrastive Learning for Efficient Visual Conceptual Pre-training—0
PFML: Self-Supervised Learning of Time-Series Data Without Representation CollapseCode0
Leveraging large language models for efficient representation learning for entity resolution—0
Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion—0
A Self-Supervised Model for Multi-modal Stroke Risk PredictionCode0
Less is More: Unseen Domain Fake News Detection via Causal Propagation Substructures—0
Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction—0
Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment—0
CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision—0
Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment—0
Aligning Visual Contrastive learning models via Preference OptimizationCode0
Prompt-enhanced Network for Hateful Meme ClassificationCode0
MoCoKGC: Momentum Contrast Entity Encoding for Knowledge Graph Completion—0
Variational Graph Contrastive LearningCode0
Decoding Visual Experience and Mapping Semantics through Whole-Brain Analysis Using fMRI Foundation ModelsCode0
Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering—0
Model Editing for LLMs4Code: How Far are We?Code0
Finding "Good Views" of Electrocardiogram Signals for Inferring Abnormalities in Cardiac ConditionCode0
Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation—0
SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking—0
Understanding the Role of Equivariance in Self-supervised LearningCode0
PRISM: Privacy-preserving Inter-Site MRI Harmonization via Disentangled Representation LearningCode0
Personalized News Recommendation System via LLM Embedding and Co-Occurrence Patterns—0
Reducing Distraction in Long-Context Language Models by Focused Learning—0
Predicting Stroke through Retinal Graphs and Multimodal Self-supervised LearningCode0
Enhancing Cardiovascular Disease Prediction through Multi-Modal Self-Supervised LearningCode0
ImpScore: A Learnable Metric For Quantifying The Implicitness Level of LanguageCode0
ACCIO: Table Understanding Enhanced via Contrastive Learning with AggregationsCode0
Judge Like a Real Doctor: Dual Teacher Sample Consistency Framework for Semi-supervised Medical Image Classification—0
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing—0
On the Comparison between Multi-modal and Single-modal Contrastive Learning—0
Understanding Contrastive Learning via Gaussian Mixture Models—0
Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud RegistrationCode0
FPPL: An Efficient and Non-IID Robust Federated Continual Learning FrameworkCode0
Fine Grained Insider Risk Detection—0
Exploring Optimal Transport-Based Multi-Grained Alignments for Text-Molecule Retrieval—0
Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional NetworksCode0
DPCL-Diff: The Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning—0
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination—0
Learning Hidden Subgoals under Temporal Ordering Constraints in Reinforcement Learning—0
OSAD: Open-Set Aircraft Detection in SAR Images—0
LEARNER: Learning Granular Labels from Coarse Labels using Contrastive Learning—0
Multi-Channel Hypergraph Contrastive Learning for Matrix Completion—0
Negative-Free Self-Supervised Gaussian Embedding of GraphsCode0
Replace-then-Perturb: Targeted Adversarial Attacks With Visual Reasoning for Vision-Language Models—0
How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method—0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified