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 3251–3260 of 6661 papers

Title	Date	Tasks	Status
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing	Nov 5, 2024	Change DetectionContrastive Learning	—Unverified
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data	Jan 18, 2023	Contrastive LearningPoint Cloud Segmentation	—Unverified
From Patches to Objects: Exploiting Spatial Reasoning for Better Visual Representations	May 21, 2023	Contrastive LearningLinear evaluation	—Unverified
Intelligently Augmented Contrastive Tensor Factorization: Empowering Multi-dimensional Time Series Classification in Low-Data Environments	May 3, 2025	Contrastive LearningTime Series	—Unverified
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning	Jun 21, 2024	Contrastive LearningData Augmentation	—Unverified
Contrastive Learning for Regression on Hyperspectral Data	Feb 12, 2024	Contrastive Learningimage-classification	—Unverified
Learning Genomic Structure from k-mers	May 22, 2025	Contrastive Learning	—Unverified
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension	Dec 9, 2021	Contrastive LearningMachine Reading Comprehension	—Unverified
From Fake to Hyperpartisan News Detection Using Domain Adaptation	Aug 4, 2023	ClusteringContrastive Learning	—Unverified
Learning Hidden Subgoals under Temporal Ordering Constraints in Reinforcement Learning	Nov 3, 2024	Contrastive Learningreinforcement-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