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 1861–1870 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
Unsupervised Visible-Infrared Person Re-Identification via Progressive Graph Matching and Alternate Learning	Jan 1, 2023	Contrastive LearningGraph Matching	CodeCode Available	1	5
Unsupervised Vision-Language Parsing: Seamlessly Bridging Visual Scene Graphs with Language Structures via Dependency Relationships	Mar 27, 2022	Contrastive LearningPhrase Grounding	CodeCode Available	1	5
Motion-aware Contrastive Video Representation Learning via Foreground-background Merging	Sep 30, 2021	Action RecognitionContrastive Learning	CodeCode Available	1	5
Multi-granularity Contrastive Cross-modal Collaborative Generation for End-to-End Long-term Video Question Answering	Oct 12, 2024	Answer GenerationBlocking	CodeCode Available	1	5
On Compositions of Transformations in Contrastive Self-Supervised Learning	Mar 9, 2020	Action RecognitionAudio Classification	CodeCode Available	1	5
Differentiable Data Augmentation for Contrastive Sentence Representation Learning	Oct 29, 2022	Contrastive LearningData Augmentation	CodeCode Available	1	5
MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification	Feb 13, 2025	Contrastive LearningEEG	CodeCode Available	1	5
Max-Margin Contrastive Learning	Dec 21, 2021	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification	Jan 9, 2023	ClassificationContrastive Learning	CodeCode Available	1	5
Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning	Dec 1, 2023	Contrastive LearningRepresentation Learning	CodeCode Available	1	5

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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