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 1741–1750 of 6661 papers

Title	Date	Tasks	Status	Hype
Effectiveness of Vision Language Models for Open-world Single Image Test Time Adaptation	Jun 1, 2024	Contrastive LearningDomain Adaptation	—Unverified	0
Heterophilous Distribution Propagation for Graph Neural Networks	May 31, 2024	Contrastive LearningGraph Mining	—Unverified	0
Self-degraded contrastive domain adaptation for industrial fault diagnosis with bi-imbalanced data	May 31, 2024	Contrastive LearningDomain Adaptation	—Unverified	0
Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learning	May 31, 2024	Contrastive LearningIntent Detection	—Unverified	0
LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis	May 31, 2024	Contrastive Learning	CodeCode Available	1
Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation Learning	May 31, 2024	Action RecognitionContrastive Learning	CodeCode Available	0
Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimation	May 31, 2024	Antibody-antigen binding predictionContrastive Learning	CodeCode Available	0
Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias	May 31, 2024	Collaborative FilteringContrastive Learning	CodeCode Available	0
GANcrop: A Contrastive Defense Against Backdoor Attacks in Federated Learning	May 31, 2024	Contrastive LearningFederated Learning	—Unverified	0
Medication Recommendation via Dual Molecular Modalities and Multi-Step Enhancement	May 30, 2024	Contrastive LearningRecommendation Systems	CodeCode Available	0

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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