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 2201–2225 of 6661 papers

TitleStatusHype
A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysis—0
Gaga: Group Any Gaussians via 3D-aware Memory Bank—0
Game State Learning via Game Scene Augmentation—0
Generalizing Supervised Contrastive learning: A Projection Perspective—0
DeepQR: Neural-based Quality Ratings for Learnersourced Multiple-Choice Questions—0
Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays—0
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond—0
Deep Learning to Predict Glaucoma Progression using Structural Changes in the Eye—0
ClinLinker: Medical Entity Linking of Clinical Concept Mentions in Spanish—0
A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters—0
Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems—0
Deep learning-based UAV detection in the low altitude clutter background—0
Clinically Labeled Contrastive Learning for OCT Biomarker Classification—0
Acoustic identification of individual animals with hierarchical contrastive learning—0
Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning—0
Deep Learning-Based Identification of Inconsistent Method Names: How Far Are We?—0
Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)—0
Clinical Contrastive Learning for Biomarker Detection—0
Statistically-informed deep learning for gravitational wave parameter estimation—0
A Survey on Contrastive Self-supervised Learning—0
Fuse after Align: Improving Face-Voice Association Learning via Multimodal Encoder—0
Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment—0
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning—0
A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks—0
Deep Graph Clustering via Mutual Information Maximization and Mixture Model—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