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

Showing 43264350 of 6661 papers

TitleStatusHype
Contrastive Representation Learning for Gaze EstimationCode1
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems0
Non-Contrastive Learning-based Behavioural Biometrics for Smart IoT Devices0
Adversarial Pretraining of Self-Supervised Deep Networks: Past, Present and Future0
Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddingsCode1
Global Contrastive Batch Sampling via Optimization on Sample PermutationsCode0
Neural Eigenfunctions Are Structured Representation LearnersCode1
Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data AugmentationCode1
A Benchmark Study of Contrastive Learning for Arabic Social MeaningCode0
Exploring Representation-Level Augmentation for Code SearchCode1
Contrastive Prototypical Network with Wasserstein Confidence PenaltyCode1
Multi-View Reasoning: Consistent Contrastive Learning for Math Word ProblemCode2
Twin Contrastive Learning for Online ClusteringCode1
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing0
GLCC: A General Framework for Graph-Level Clustering0
BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification0
Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem0
Apple of Sodom: Hidden Backdoors in Superior Sentence Embeddings via Contrastive Learning0
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP ModelsCode0
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer EnsembleCode0
SSiT: Saliency-guided Self-supervised Image Transformer for Diabetic Retinopathy GradingCode1
Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?Code1
Controller-Guided Partial Label Consistency Regularization with Unlabeled Data0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified