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 1041–1050 of 6661 papers

Title	Date	Tasks	Status	Hype
Self-Supervised Graph Structure Refinement for Graph Neural Networks	Nov 12, 2022	Contrastive LearningGPU	CodeCode Available	1
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning	Nov 12, 2022	Contrastive LearningDomain Adaptation	CodeCode Available	1
English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings	Nov 11, 2022	Contrastive LearningRetrieval	CodeCode Available	1
Unbiased Supervised Contrastive Learning	Nov 10, 2022	Contrastive Learning	CodeCode Available	1
Efficient Zero-shot Event Extraction with Context-Definition Alignment	Nov 9, 2022	Contrastive LearningEvent Extraction	CodeCode Available	1
miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings	Nov 9, 2022	Contrastive LearningFew-Shot Learning	CodeCode Available	1
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection	Nov 8, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Black-Box Attack against GAN-Generated Image Detector with Contrastive Perturbation	Nov 7, 2022	Contrastive LearningDecoder	CodeCode Available	1
Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects	Nov 4, 2022	Contrastive LearningDisentanglement	CodeCode Available	1
SLICER: Learning universal audio representations using low-resource self-supervised pre-training	Nov 2, 2022	Audio ClassificationClustering	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