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 1811–1820 of 6661 papers

Title	Date	Tasks	Status	Hype
G-SimCLR : Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling	Sep 25, 2020	Contrastive LearningDenoising	CodeCode Available	1
Label-Efficient Multi-Task Segmentation using Contrastive Learning	Sep 23, 2020	Contrastive LearningMulti-Task Learning	CodeCode Available	1
Contrastive Clustering	Sep 21, 2020	ClusteringContrastive Learning	CodeCode Available	1
DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition	Sep 20, 2020	Contrastive LearningDiversity	CodeCode Available	1
A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection	Sep 18, 2020	Contrastive LearningTopic Models	CodeCode Available	1
MoPro: Webly Supervised Learning with Momentum Prototypes	Sep 17, 2020	Contrastive Learningimage-classification	CodeCode Available	1
Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization	Sep 16, 2020	Autonomous DrivingContrastive Learning	CodeCode Available	1
Group-wise Contrastive Learning for Neural Dialogue Generation	Sep 16, 2020	Contrastive LearningDialogue Generation	CodeCode Available	1
A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification	Sep 5, 2020	Contrastive LearningPerson Re-Identification	CodeCode Available	1
Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification	Sep 2, 2020	Contrastive LearningGeneral Classification	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