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 3371–3380 of 6661 papers

Title	Date	Tasks	Status
Enhanced Multimodal Representation Learning with Cross-modal KD	Jun 13, 2023	Contrastive LearningEmotion Classification	—Unverified
Enhanced Soft Label for Semi-Supervised Semantic Segmentation	Jan 1, 2023	Contrastive LearningPseudo Label	—Unverified
Enhanced Unsupervised Image-to-Image Translation Using Contrastive Learning and Histogram of Oriented Gradients	Sep 24, 2024	Contrastive LearningImage Generation	—Unverified
Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security	Feb 2, 2024	Adversarial AttackCollaborative Filtering	—Unverified
Enhanced Urdu Intent Detection with Large Language Models and Prototype-Informed Predictive Pipelines	May 8, 2025	Contrastive LearningFew-Shot Learning	—Unverified
Enhance Exploration in Safe Reinforcement Learning with Contrastive Representation Learning	Mar 13, 2025	Contrastive LearningRepresentation Learning	—Unverified
Enhancement of Dysarthric Speech Reconstruction by Contrastive Learning	Oct 5, 2024	Contrastive Learningspeech-recognition	—Unverified
Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation	May 22, 2024	Active LearningContrastive Learning	—Unverified
Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning	Dec 27, 2024	Adversarial RobustnessContrastive Learning	—Unverified
Enhancing Conceptual Understanding in Multimodal Contrastive Learning through Hard Negative Samples	Mar 5, 2024	Concept AlignmentContrastive 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