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 3501–3525 of 6661 papers

TitleStatusHype
Single-temporal Supervised Remote Change Detection for Domain Generalization—0
Reuse out-of-year data to enhance land cover mappingvia feature disentanglement and contrastive learning—0
Multimodal 3D Object Detection on Unseen Domains—0
Contextrast: Contextual Contrastive Learning for Semantic Segmentation—0
EMC^2: Efficient MCMC Negative Sampling for Contrastive Learning with Global ConvergenceCode0
Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation—0
Contrastive Mean-Shift Learning for Generalized Category Discovery—0
Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labels—0
Fuse after Align: Improving Face-Voice Association Learning via Multimodal Encoder—0
Real-world Instance-specific Image Goal Navigation: Bridging Domain Gaps via Contrastive Learning—0
Learning Tracking Representations from Single Point Annotations—0
Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition—0
An Experimental Comparison Of Multi-view Self-supervised Methods For Music TaggingCode0
GCC: Generative Calibration Clustering—0
RoNID: New Intent Discovery with Generated-Reliable Labels and Cluster-friendly Representations—0
Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification—0
HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies—0
CodeFort: Robust Training for Code Generation Models—0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
Can Contrastive Learning Refine Embeddings—0
PromptSync: Bridging Domain Gaps in Vision-Language Models through Class-Aware Prototype Alignment and Discrimination—0
Contrastive-Based Deep Embeddings for Label Noise-Resilient Histopathology Image ClassificationCode0
Context-aware Video Anomaly Detection in Long-Term Datasets—0
Global Contrastive Training for Multimodal Electronic Health Records with Language Supervision—0
Unsupervised Visible-Infrared ReID via Pseudo-label Correction and Modality-level Alignment—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