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
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated LearningCode2
QoS-Aware Graph Contrastive Learning for Web Service Recommendation—0
Preserving Silent Features for Domain Generalization—0
Exploiting Data Hierarchy as a New Modality for Contrastive Learning—0
Enhancing Context Through Contrast—0
Unsupervised hard Negative Augmentation for contrastive learningCode0
Multi-Stage Contrastive Regression for Action Quality AssessmentCode0
Graph-level Protein Representation Learning by Structure Knowledge Refinement—0
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing—0
Learning Multimodal Volumetric Features for Large-Scale Neuron TracingCode0
Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-IdentificationCode1
Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation—0
Task Oriented Dialogue as a Catalyst for Self-Supervised Automatic Speech RecognitionCode0
Balancing Continual Learning and Fine-tuning for Human Activity Recognition—0
Multi-modal vision-language model for generalizable annotation-free pathology localization and clinical diagnosisCode1
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning—0
SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks—0
Multimodal self-supervised learning for lesion localization—0
MLIP: Medical Language-Image Pre-training with Masked Local Representation Learning—0
ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual ClassificationCode0
AliFuse: Aligning and Fusing Multi-modal Medical Data for Computer-Aided DiagnosisCode0
Unsupervised Continual Anomaly Detection with Contrastively-learned PromptCode2
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive LearningCode1
Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces—0
A Novel Transformer-Based Self-Supervised Learning Method to Enhance Photoplethysmogram Signal Artifact Detection—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