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 2531–2540 of 6661 papers

Title	Date	Tasks	Status
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis	Dec 24, 2024	Contrastive LearningFederated Learning	—Unverified
Text-Driven Tumor Synthesis	Dec 24, 2024	Contrastive LearningSensitivity	—Unverified
Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning	Dec 23, 2024	Contrastive LearningTopic Models	—Unverified
Multiple Consistency-guided Test-Time Adaptation for Contrastive Audio-Language Models with Unlabeled Audio	Dec 23, 2024	Contrastive LearningPrompt Learning	—Unverified
Adaptive Dataset Quantization	Dec 22, 2024	Contrastive LearningDataset Distillation	—Unverified
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction	Dec 21, 2024	Anomaly DetectionContrastive Learning	—Unverified
Open-Vocabulary Mobile Manipulation Based on Double Relaxed Contrastive Learning with Dense Labeling	Dec 21, 2024	Contrastive LearningImage Retrieval	CodeCode Available
Leveraging Contrastive Learning for Semantic Segmentation with Consistent Labels Across Varying Appearances	Dec 21, 2024	Contrastive LearningDomain Adaptation	—Unverified
FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis	Dec 21, 2024	Contrastive LearningData Augmentation	—Unverified
Enhancing Contrastive Learning Inspired by the Philosophy of "The Blind Men and the Elephant"	Dec 21, 2024	Contrastive LearningData Augmentation	CodeCode Available

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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