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 1381–1390 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
Modeling Video As Stochastic Processes for Fine-Grained Video Representation Learning	Jan 1, 2023	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators	Sep 21, 2021	Contrastive LearningGraph Representation Learning	CodeCode Available	1	5
Efficient fine-tuning methodology of text embedding models for information retrieval: contrastive learning penalty (clp)	Dec 23, 2024	Contrastive LearningInformation Retrieval	CodeCode Available	1	5
Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning	Sep 10, 2021	Continual PretrainingContrastive Learning	CodeCode Available	1	5
Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection	Feb 18, 2025	Contrastive LearningDomain Generalization	CodeCode Available	1	5
MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition	Apr 3, 2023	Action RecognitionContrastive Learning	CodeCode Available	1	5
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling	Oct 7, 2021	Contrastive Learningslot-filling	CodeCode Available	1	5
From t-SNE to UMAP with contrastive learning	Jun 3, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
Efficient Non-Local Contrastive Attention for Image Super-Resolution	Jan 11, 2022	Contrastive LearningFeature Correlation	CodeCode Available	1	5
Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction	Apr 28, 2021	Contrastive LearningData Augmentation	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