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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 4791–4800 of 6661 papers

Title	Date	Tasks	Status
Adaptive Similarity Bootstrapping for Self-Distillation based Representation Learning	Mar 23, 2023	Contrastive LearningRepresentation Learning	CodeCode Available
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection	Mar 23, 2023	Anomaly DetectionContrastive Learning	—Unverified
SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology	Mar 23, 2023	Contrastive Learningimbalanced classification	—Unverified
Low-Light Image Enhancement by Learning Contrastive Representations in Spatial and Frequency Domains	Mar 23, 2023	Contrastive LearningImage Enhancement	—Unverified
Preventing Dimensional Collapse of Incomplete Multi-View Clustering via Direct Contrastive Learning	Mar 22, 2023	ClusteringContrastive Learning	—Unverified
CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning	Mar 22, 2023	Contrastive LearningRetrieval	—Unverified
Multi-view Feature Extraction based on Triple Contrastive Heads	Mar 22, 2023	Contrastive LearningSelf-Supervised Learning	—Unverified
Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder	Mar 22, 2023	Contrastive LearningManagement	—Unverified
Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioning	Mar 22, 2023	Contrastive LearningDomain Adaptation	—Unverified
CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks	Mar 21, 2023	Contrastive LearningSurvival Analysis	—Unverified

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All datasets imagenet-1k 10,000 People - Human Pose Recognition Data CIFAR-10 STL-10

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet50	ImageNet Top-1 Accuracy	73.6	—	Unverified
2	ResNet50	ImageNet Top-1 Accuracy	73	—	Unverified
3	ResNet50	ImageNet Top-1 Accuracy	71.1	—	Unverified
4	ResNet50	ImageNet Top-1 Accuracy	69.3	—	Unverified
5	ResNet50 (v2)	ImageNet Top-1 Accuracy	67.6	—	Unverified
6	ResNet50 (v2)	ImageNet Top-1 Accuracy	63.8	—	Unverified
7	ResNet50	ImageNet Top-1 Accuracy	63.6	—	Unverified
8	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
9	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
10	ResNet50 (4×)	ImageNet Top-1 Accuracy	61.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1	0..5sec	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	84.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	85.55	—	Unverified