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 4941–4950 of 6661 papers

Title	Date	Tasks	Status
Leveraging Task Dependency and Contrastive Learning for Case Outcome Classification on European Court of Human Rights Cases	Feb 1, 2023	ArticlesContrastive Learning	—Unverified
A Deep Behavior Path Matching Network for Click-Through Rate Prediction	Feb 1, 2023	Click-Through Rate PredictionContrastive Learning	—Unverified
NoiseTransfer: Image Noise Generation with Contrastive Embeddings	Jan 31, 2023	Contrastive LearningDenoising	CodeCode Available
Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain Adaptation	Jan 31, 2023	ClusteringContrastive Learning	CodeCode Available
NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks	Jan 31, 2023	Contrastive LearningEvolutionary Algorithms	CodeCode Available
Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning	Jan 31, 2023	Contrastive LearningNode Classification	CodeCode Available
Massively Scaling Heteroscedastic Classifiers	Jan 30, 2023	ClassificationContrastive Learning	—Unverified
SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking	Jan 29, 2023	Contrastive LearningSelf-Supervised Learning	—Unverified
The Influences of Color and Shape Features in Visual Contrastive Learning	Jan 29, 2023	Contrastive LearningData Augmentation	—Unverified
Unbiased and Efficient Self-Supervised Incremental Contrastive Learning	Jan 28, 2023	Contrastive LearningGraph Representation Learning	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