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 3801–3810 of 6661 papers

Title	Date	Tasks	Status	Hype
NCL: Textual Backdoor Defense Using Noise-augmented Contrastive Learning	Mar 3, 2023	backdoor defenseContrastive Learning	—Unverified	0
Generalized Semantic Segmentation by Self-Supervised Source Domain Projection and Multi-Level Contrastive Learning	Mar 3, 2023	Contrastive LearningDomain Generalization	CodeCode Available	0
Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning	Mar 3, 2023	Contrastive LearningDecision Making	—Unverified	0
On the Provable Advantage of Unsupervised Pretraining	Mar 2, 2023	Contrastive LearningRepresentation Learning	—Unverified	0
Hierarchical discriminative learning improves visual representations of biomedical microscopy	Mar 2, 2023	Contrastive LearningRepresentation Learning	—Unverified	0
Heterogeneous Graph Contrastive Learning for Recommendation	Mar 2, 2023	Contrastive LearningRecommendation Systems	CodeCode Available	1
Multi-Task Self-Supervised Time-Series Representation Learning	Mar 2, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation	Mar 2, 2023	Contrastive LearningDialogue Generation	CodeCode Available	0
Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning	Mar 2, 2023	Action RecognitionAutonomous Driving	—Unverified	0
Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning	Mar 2, 2023	Contrastive LearningCross-Domain Few-Shot	CodeCode Available	1

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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