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 4921–4930 of 6661 papers

Title	Date	Tasks	Status
SimCGNN: Simple Contrastive Graph Neural Network for Session-based Recommendation	Feb 8, 2023	Contrastive LearningGraph Neural Network	—Unverified
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection	Feb 8, 2023	Contrastive Learning	CodeCode Available
Cluster-aware Contrastive Learning for Unsupervised Out-of-distribution Detection	Feb 6, 2023	Contrastive LearningOut-of-Distribution Detection	—Unverified
Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning	Feb 6, 2023	Contrastive Learning	—Unverified
Spectral Augmentations for Graph Contrastive Learning	Feb 6, 2023	Contrastive LearningGraph Embedding	—Unverified
Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking	Feb 6, 2023	Contrastive Learning	—Unverified
APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning	Feb 6, 2023	Contrastive LearningLanguage Modeling	—Unverified
CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning	Feb 5, 2023	Contrastive LearningData Augmentation	—Unverified
Adversarial Learning Data Augmentation for Graph Contrastive Learning in Recommendation	Feb 5, 2023	Contrastive LearningData Augmentation	CodeCode Available
Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition	Feb 5, 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