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 3226–3250 of 6661 papers

TitleStatusHype
Cross-Lingual Word Alignment for ASEAN Languages with Contrastive Learning—0
A New Brain Network Construction Paradigm for Brain Disorder via Diffusion-based Graph Contrastive Learning—0
Enhanced Long-Tailed Recognition with Contrastive CutMix AugmentationCode0
Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for RecommendationsCode0
HCS-TNAS: Hybrid Constraint-driven Semi-supervised Transformer-NAS for Ultrasound Image Segmentation—0
Leveraging Graph Structures to Detect Hallucinations in Large Language ModelsCode0
DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts—0
MedRAT: Unpaired Medical Report Generation via Auxiliary Tasks—0
Align and Aggregate: Compositional Reasoning with Video Alignment and Answer Aggregation for Video Question-Answering—0
FlowCon: Out-of-Distribution Detection using Flow-Based Contrastive LearningCode0
Supporting Cross-language Cross-project Bug Localization Using Pre-trained Language Models—0
Towards Attention-based Contrastive Learning for Audio Spoof Detection—0
DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing—0
A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces—0
Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images—0
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection—0
Semantic Compositions Enhance Vision-Language Contrastive Learning—0
CGRclust: Chaos Game Representation for Twin Contrastive Clustering of Unlabelled DNA SequencesCode0
SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTsCode0
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
Enhancing Travel Decision-Making: A Contrastive Learning Approach for Personalized Review Rankings in Accommodations—0
LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement—0
InfoNCE: Identifying the Gap Between Theory and Practice—0
eMoE-Tracker: Environmental MoE-based Transformer for Robust Event-guided Object Tracking—0
Zero-shot domain adaptation based on dual-level mix and contrast—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