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 4281–4290 of 6661 papers

Title	Date	Tasks	Status
Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning	Sep 13, 2023	Contrastive LearningGeneralized Zero-Shot Learning	—Unverified
Domain-Aware Augmentations for Unsupervised Online General Continual Learning	Sep 13, 2023	Continual LearningContrastive Learning	—Unverified
Multi-behavior Recommendation with SVD Graph Neural Networks	Sep 13, 2023	Contrastive LearningRecommendation Systems	—Unverified
Grounded Language Acquisition From Object and Action Imagery	Sep 12, 2023	Action RecognitionContrastive Learning	—Unverified
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model	Sep 12, 2023	AttributeContrastive Learning	CodeCode Available
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning	Sep 11, 2023	Contrastive LearningHyperedge Prediction	CodeCode Available
ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space	Sep 11, 2023	Collision AvoidanceContrastive Learning	—Unverified
Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals	Sep 11, 2023	Contrastive LearningMedical Diagnosis	—Unverified
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition	Sep 11, 2023	Action RecognitionContrastive Learning	—Unverified
Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition	Sep 10, 2023	Action RecognitionContrastive Learning	—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