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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1601–1625 of 6661 papers

TitleStatusHype
ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection—0
Semantic Compositions Enhance Vision-Language Contrastive Learning—0
Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution AnalysisCode2
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTsCode0
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
ProtoGMM: Multi-prototype Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation—0
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers—0
Rethinking and Defending Protective Perturbation in Personalized Diffusion ModelsCode1
Zero-shot domain adaptation based on dual-level mix and contrast—0
Local Manifold Learning for No-Reference Image Quality Assessment—0
Foundation Models for ECG: Leveraging Hybrid Self-Supervised Learning for Advanced Cardiac Diagnostics—0
Denoising as Adaptation: Noise-Space Domain Adaptation for Image RestorationCode2
Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive LearningCode0
Selective Prompting Tuning for Personalized Conversations with LLMsCode1
Data curation via joint example selection further accelerates multimodal learning—0
Investigating Self-Supervised Methods for Label-Efficient Learning—0
MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation LearningCode0
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System—0
Retrieval-style In-Context Learning for Few-shot Hierarchical Text ClassificationCode1
Contrastive General Graph Matching with Adaptive Augmentation Sampling—0
Video Inpainting Localization with Contrastive LearningCode1
Show:102550
← PrevPage 65 of 267Next →
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