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 1691–1700 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding	Oct 6, 2022	3D Object ClassificationContrastive Learning	CodeCode Available	1	5
A Simple Long-Tailed Recognition Baseline via Vision-Language Model	Nov 29, 2021	Contrastive LearningLanguage Modeling	CodeCode Available	1	5
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain	Jul 2, 2020	Contrastive LearningData Augmentation	CodeCode Available	1	5
InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive Learning	Jul 9, 2020	Contrastive LearningImage Generation	CodeCode Available	1	5
Low-rank Prompt Interaction for Continual Vision-Language Retrieval	Jan 24, 2025	Continual LearningContrastive Learning	CodeCode Available	1	5
Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation	Oct 22, 2023	Contrastive LearningData Augmentation	CodeCode Available	1	5
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities	May 17, 2025	Brain Tumor SegmentationContrastive Learning	CodeCode Available	1	5
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least	Feb 18, 2023	Contrastive LearningOpen-Ended Question Answering	CodeCode Available	1	5
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction	May 19, 2022	Contrastive LearningRelation	CodeCode Available	1	5
Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks	Jan 31, 2023	Contrastive LearningGraph 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