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 2201–2250 of 6661 papers

TitleStatusHype
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—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
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
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
3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation—0
GCC: Generative Calibration Clustering—0
DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction—0
Deep Fraud Detection on Non-attributed Graph—0
CLICv2: Image Complexity Representation via Content Invariance Contrastive Learning—0
DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning—0
Click-through Rate Prediction with Auto-Quantized Contrastive Learning—0
A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond—0
Deep Contrastive Multi-view Clustering under Semantic Feature Guidance—0
Understanding Deep Contrastive Learning via Coordinate-wise Optimization—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
Deep Contrastive Multiview Network Embedding—0
Deep Contrastive Learning for Feature Alignment: Insights from Housing-Household Relationship Inference—0
A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data—0
Deep Contrastive Graph Representation via Adaptive Homotopy Learning—0
Deep Contrastive Graph Learning with Clustering-Oriented Guidance—0
CLGNN: A Contrastive Learning-based GNN Model for Betweenness Centrality Prediction on Temporal Graphs—0
AGPNet -- Autonomous Grading Policy Network—0
Deep Continuous Prompt for Contrastive Learning of Sentence Embeddings—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
Deep Code Search with Naming-Agnostic Contrastive Multi-View Learning—0
CLExtract: Recovering Highly Corrupted DVB/GSE Satellite Stream with Contrastive Learning—0
A Co-training Approach for Noisy Time Series Learning—0
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation—0
Gaze Estimation with Eye Region Segmentation and Self-Supervised Multistream Learning—0
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels—0
Deep Clustering by Semantic Contrastive Learning—0
DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks—0
CLERF: Contrastive LEaRning for Full Range Head Pose Estimation—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