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 2426–2450 of 6661 papers

TitleStatusHype
Deep Learning-Based Identification of Inconsistent Method Names: How Far Are We?—0
Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science—0
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer AnalysisCode0
Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric VideosCode0
Unified 3D MRI Representations via Sequence-Invariant Contrastive LearningCode0
Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification—0
Panoramic Interests: Stylistic-Content Aware Personalized Headline GenerationCode0
Fact-Preserved Personalized News Headline GenerationCode0
Score Combining for Contrastive OOD Detection—0
SCFCRC: Simultaneously Counteract Feature Camouflage and Relation Camouflage for Fraud Detection—0
Disentangled Modeling of Preferences and Social Influence for Group RecommendationCode0
Advancing Multi-Party Dialogue Framework with Speaker-ware Contrastive Learning—0
DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample Attention within Contrastive FrameworkCode0
Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation SpaceCode0
ACCEPT: Diagnostic Forecasting of Battery Degradation Through Contrastive Learning—0
Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for Image Restoration Models Compression—0
Strategic Base Representation Learning via Feature Augmentations for Few-Shot Class Incremental Learning—0
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive LearningCode0
Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning—0
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data—0
TCMM: Token Constraint and Multi-Scale Memory Bank of Contrastive Learning for Unsupervised Person Re-identificationCode0
Homophily-aware Heterogeneous Graph Contrastive Learning—0
Molecular Graph Contrastive Learning with Line GraphCode0
Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval—0
SHYI: Action Support for Contrastive Learning in High-Fidelity Text-to-Image Generation—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