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 1121–1130 of 6661 papers

Title	Date	Tasks	Status	Hype
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation	Sep 23, 2022	Collaborative FilteringContrastive Learning	CodeCode Available	1
CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation	Sep 23, 2022	Contrastive LearningImage Segmentation	CodeCode Available	1
Capsule Network based Contrastive Learning of Unsupervised Visual Representations	Sep 22, 2022	Contrastive Learningimage-classification	CodeCode Available	1
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding	Sep 22, 2022	Contrastive LearningVideo Grounding	CodeCode Available	1
Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial Images	Sep 21, 2022	Contrastive LearningImage Augmentation	CodeCode Available	1
Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings	Sep 20, 2022	ClusteringContrastive Learning	CodeCode Available	1
SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning	Sep 20, 2022	Contrastive LearningEEG	CodeCode Available	1
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence	Sep 16, 2022	Contrastive Learning	CodeCode Available	1
MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning	Sep 16, 2022	Contrastive LearningMeta-Learning	CodeCode Available	1
Spatial-then-Temporal Self-Supervised Learning for Video Correspondence	Sep 16, 2022	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1

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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