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 1851–1860 of 6661 papers

Title	Date	Tasks	Status	Hype
Contrastive Learning for Weakly Supervised Phrase Grounding	Jun 17, 2020	Contrastive LearningLanguage Modeling	CodeCode Available	1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training	Jun 17, 2020	Contrastive LearningGeneral Classification	CodeCode Available	1
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction	Jun 15, 2020	ClusteringContrastive Learning	CodeCode Available	1
Adversarial Self-Supervised Contrastive Learning	Jun 13, 2020	Adversarial AttackContrastive Learning	CodeCode Available	1
Knowledge Distillation Meets Self-Supervision	Jun 12, 2020	Contrastive LearningKnowledge Distillation	CodeCode Available	1
Quasi-Dense Similarity Learning for Multiple Object Tracking	Jun 11, 2020	Contrastive LearningMetric Learning	CodeCode Available	1
DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors	Jun 10, 2020	AttributeContrastive Learning	CodeCode Available	1
Deep Graph Contrastive Representation Learning	Jun 7, 2020	AttributeContrastive Learning	CodeCode Available	1
Multi-view Contrastive Learning for Online Knowledge Distillation	Jun 7, 2020	ClassificationContrastive Learning	CodeCode Available	1
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations	Jun 5, 2020	ClusteringContrastive Learning	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