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 751–800 of 6661 papers

TitleStatusHype
Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning—0
Extended Cross-Modality United Learning for Unsupervised Visible-Infrared Person Re-identification—0
Multi-view Fake News Detection Model Based on Dynamic Hypergraph—0
Intra- and Inter-modal Context Interaction Modeling for Conversational Speech SynthesisCode0
Text-Driven Tumor Synthesis—0
Contrastive Representation for Interactive RecommendationCode0
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis—0
Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp)Code1
Multiple Consistency-guided Test-Time Adaptation for Contrastive Audio-Language Models with Unlabeled Audio—0
Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning—0
Adaptive Dataset Quantization—0
Trusted Mamba Contrastive Network for Multi-View ClusteringCode1
Open-Vocabulary Mobile Manipulation Based on Double Relaxed Contrastive Learning with Dense LabelingCode0
FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis—0
Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances—0
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction—0
Enhancing Contrastive Learning Inspired by the Philosophy of "The Blind Men and the Elephant"Code0
Graph Structure Refinement with Energy-based Contrastive Learning—0
SGAC: A Graph Neural Network Framework for Imbalanced and Structure-Aware AMP Classification—0
Contrastive Learning for Task-Independent SpeechLLM-PretrainingCode0
Personalized Representation from Personalized GenerationCode2
SaliencyI2PLoc: saliency-guided image-point cloud localization using contrastive learning—0
Defeasible Visual Entailment: Benchmark, Evaluator, and Reward-Driven OptimizationCode1
MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic ClassificationCode1
Learning Visual Composition through Improved Semantic Guidance—0
DiffSim: Taming Diffusion Models for Evaluating Visual SimilarityCode1
Video Prediction Policy: A Generalist Robot Policy with Predictive Visual RepresentationsCode3
WildSAT: Learning Satellite Image Representations from Wildlife Observations—0
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question AnsweringCode0
DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation—0
Balanced Gradient Sample Retrieval for Enhanced Knowledge Retention in Proxy-based Continual Learning—0
CLDG: Contrastive Learning on Dynamic GraphsCode1
ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting—0
GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive LearningCode0
What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood ContextCode0
Multi-view Granular-ball Contrastive Clustering—0
Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language ModelsCode1
I0T: Embedding Standardization Method Towards Zero Modality GapCode1
MixRec: Heterogeneous Graph Collaborative FilteringCode1
Cluster-guided Contrastive Class-imbalanced Graph Classification—0
ClarityEthic: Explainable Moral Judgment Utilizing Contrastive Ethical Insights from Large Language Models—0
CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information RetrievalCode1
Multi-Domain Features Guided Supervised Contrastive Learning for Radar Target Detection—0
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning—0
DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image SegmentationCode1
Multi-head attention debiasing and contrastive learning for mitigating Dataset Artifacts in Natural Language Inference—0
Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced RegressionCode0
Personalized LLM for Generating Customized Responses to the Same Query from Different UsersCode0
Generalization Analysis for Deep Contrastive Representation Learning—0
Temporal Contrastive Learning for Video Temporal Reasoning in Large Vision-Language Models—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