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 1201–1210 of 6661 papers

Title	Date	Tasks	Status	Hype
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning	Jul 4, 2022	AttributeContrastive Learning	CodeCode Available	1
Deep Contrastive One-Class Time Series Anomaly Detection	Jul 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition	Jul 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection	Jul 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text Recognition	Jul 1, 2022	Contrastive LearningScene Text Recognition	CodeCode Available	1
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label	Jul 1, 2022	Adversarial AttackBackdoor Attack	CodeCode Available	1
Self-Supervised Learning for Multimedia Recommendation	Jun 30, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image Classification	Jun 28, 2022	Contrastive LearningFederated Learning	CodeCode Available	1
Reducing Annotation Need in Self-Explanatory Models for Lung Nodule Diagnosis	Jun 27, 2022	Clinical KnowledgeContrastive Learning	CodeCode Available	1
Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning	Jun 26, 2022	Contrastive LearningDecoder	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