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

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Showing 3541–3550 of 6661 papers

Title	Date	Tasks	Status
FedSSC: Shared Supervised-Contrastive Federated Learning	Jan 14, 2023	Contrastive LearningFederated Learning	—Unverified
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis	Dec 24, 2024	Contrastive LearningFederated Learning	—Unverified
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services	Oct 7, 2024	Contrastive LearningPrivacy Preserving	—Unverified
Fermi-Bose Machine achieves both generalization and adversarial robustness	Apr 21, 2024	Adversarial RobustnessContrastive Learning	—Unverified
Few-Example Clustering via Contrastive Learning	Jul 8, 2022	ClusteringContrastive Learning	—Unverified
Few-Shot Classification with Contrastive Learning	Sep 17, 2022	ClassificationContrastive Learning	—Unverified
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images	Sep 5, 2024	Activity RecognitionContinual Learning	—Unverified
Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning	Feb 21, 2023	Anomaly DetectionContrastive Learning	—Unverified
Few-shot Font Generation by Learning Style Difference and Similarity	Jan 24, 2023	Contrastive LearningFont Generation	—Unverified
Few-shot Implicit Function Generation via Equivariance	Jan 3, 2025	Contrastive Learning	—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