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 3331–3340 of 6661 papers

Title	Date	Tasks	Status
Efficient Building Roof Type Classification: A Domain-Specific Self-Supervised Approach	Mar 28, 2025	Computational EfficiencyContrastive Learning	—Unverified
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning	May 25, 2023	Contrastive LearningInformation Retrieval	—Unverified
Efficient Domain Adaptation of Multimodal Embeddings using Constrastive Learning	Feb 4, 2025	Contrastive LearningDomain Adaptation	—Unverified
Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning	Jan 16, 2025	Contrastive LearningFew-Shot Learning	—Unverified
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations	Sep 6, 2020	ClusteringCode Summarization	—Unverified
Efficient Selective Audio Masked Multimodal Bottleneck Transformer for Audio-Video Classification	Jan 8, 2024	Action RecognitionContrastive Learning	—Unverified
Efficient Test-Time Prompt Tuning for Vision-Language Models	Aug 11, 2024	Contrastive LearningPrompt Learning	—Unverified
Efficient Text Encoders for Labor Market Analysis	May 30, 2025	Contrastive LearningExtreme Multi-Label Classification	—Unverified
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning	Mar 24, 2024	Contrastive LearningData Augmentation	—Unverified
Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning	Mar 2, 2023	Action RecognitionAutonomous Driving	—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