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 4531–4540 of 6661 papers

Title	Date	Tasks	Status	Hype
Spatial-then-Temporal Self-Supervised Learning for Video Correspondence	Sep 16, 2022	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection	Sep 16, 2022	Contrastive LearningOut-of-Distribution Detection	—Unverified	0
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation	Sep 14, 2022	Contrastive Learningdialog state tracking	—Unverified	0
FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning	Sep 14, 2022	Contrastive LearningGaze Estimation	—Unverified	0
Graph Contrastive Learning with Personalized Augmentation	Sep 14, 2022	Contrastive LearningData Augmentation	—Unverified	0
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding	Sep 14, 2022	Contrastive LearningSTS	—Unverified	0
Jointly Contrastive Representation Learning on Road Network and Trajectory	Sep 14, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1
Joint Debiased Representation and Image Clustering Learning with Self-Supervision	Sep 14, 2022	ClusteringContrastive Learning	—Unverified	0
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack	Sep 14, 2022	3D ClassificationAdversarial Attack	CodeCode Available	0
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning	Sep 13, 2022	Contrastive LearningDeep Reinforcement Learning	—Unverified	0

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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