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

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Showing 2771–2780 of 6661 papers

Title	Date	Tasks	Status
Gradient Regularized Contrastive Learning for Continual Domain Adaptation	Mar 23, 2021	Contrastive LearningDomain Adaptation	—Unverified
Improving Deep Embedded Clustering via Learning Cluster-level Representations	Oct 1, 2022	ClusteringContrastive Learning	—Unverified
Contrastive Learning to Improve Retrieval for Real-world Fact Checking	Oct 7, 2024	Contrastive LearningFact Checking	—Unverified
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach	Aug 2, 2023	Anomaly DetectionContrastive Learning	—Unverified
Improving Dense Contrastive Learning with Dense Negative Pairs	Oct 11, 2022	Contrastive LearningMulti-Label Classification	—Unverified
Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning	Apr 30, 2024	Contrastive LearningLanguage Modeling	—Unverified
Improving Generalizability of Protein Sequence Models via Data Augmentations	Jan 1, 2021	BIG-bench Machine LearningContrastive Learning	—Unverified
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records	Aug 24, 2023	Contrastive LearningSurvival Analysis	—Unverified
GraphCL: Contrastive Self-Supervised Learning of Graph Representations	Jul 15, 2020	Contrastive LearningInductive Learning	—Unverified
Advancing Melanoma Diagnosis with Self-Supervised Neural Networks: Evaluating the Effectiveness of Different Techniques	Jul 19, 2024	Contrastive LearningMelanoma Diagnosis	—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