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 3211–3220 of 6661 papers

Title	Date	Tasks	Status
Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing	Oct 26, 2022	Contrastive LearningState Estimation	—Unverified
FSCIL-SEI: Few-Shot Class-Incremental Learning Approach for Specific Emitter Identification	Mar 3, 2025	class-incremental learningClass Incremental Learning	—Unverified
Channel-Wise Contrastive Learning for Learning with Noisy Labels	Aug 14, 2023	Contrastive LearningLearning with noisy labels	—Unverified
InfoGCL: Information-Aware Graph Contrastive Learning	Oct 28, 2021	Contrastive LearningGraph Classification	—Unverified
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach	Feb 11, 2022	Anomaly DetectionContrastive Learning	—Unverified
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency	Jan 1, 2022	Contrastive LearningSelf-Supervised Learning	—Unverified
Boosting Star-GANs for Voice Conversion with Contrastive Discriminator	Sep 21, 2022	Contrastive LearningVoice Conversion	—Unverified
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts	Oct 8, 2021	Clone DetectionContrastive Learning	—Unverified
3D-Aware Encoding for Style-based Neural Radiance Fields	Nov 12, 2022	Contrastive LearningImage Reconstruction	—Unverified
From Real Artifacts to Virtual Reference: A Robust Framework for Translating Endoscopic Images	Oct 15, 2024	Contrastive LearningDenoising	—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