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 751–775 of 6661 papers

TitleStatusHype
Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning—0
Multi-view Fake News Detection Model Based on Dynamic Hypergraph—0
Extended Cross-Modality United Learning for Unsupervised Visible-Infrared Person Re-identification—0
Intra- and Inter-modal Context Interaction Modeling for Conversational Speech SynthesisCode0
Contrastive Representation for Interactive RecommendationCode0
Text-Driven Tumor Synthesis—0
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis—0
Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning—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
Adaptive Dataset Quantization—0
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
Trusted Mamba Contrastive Network for Multi-View ClusteringCode1
Enhancing Contrastive Learning Inspired by the Philosophy of "The Blind Men and the Elephant"Code0
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction—0
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
Video Prediction Policy: A Generalist Robot Policy with Predictive Visual RepresentationsCode3
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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