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 1441–1450 of 6661 papers

Title	Date	Tasks	Status	Hype
SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-Training for Spatial-Aware Visual Representations	Dec 9, 2021	Contrastive LearningUnsupervised Pre-training	CodeCode Available	1
Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy Detection	Dec 8, 2021	Contrastive LearningCopy Detection	CodeCode Available	1
TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning	Dec 7, 2021	Action RecognitionContrastive Learning	CodeCode Available	1
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition	Dec 7, 2021	Action RecognitionContrastive Learning	CodeCode Available	1
Joint Learning of Localized Representations from Medical Images and Reports	Dec 6, 2021	Contrastive Learningimage-classification	CodeCode Available	1
Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation	Dec 6, 2021	Contrastive Learning	CodeCode Available	1
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning	Dec 6, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	1
VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning	Dec 5, 2021	Contrastive LearningLearning Semantic Representations	CodeCode Available	1
Transferring Unconditional to Conditional GANs with Hyper-Modulation	Dec 4, 2021	Conditional Image GenerationContrastive Learning	CodeCode Available	1
Contrastive Cross-domain Recommendation in Matching	Dec 2, 2021	Contrastive LearningRepresentation 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