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 1241–1250 of 6661 papers

Title	Date	Tasks	Status	Hype
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series	May 31, 2022	Contrastive LearningDimensionality Reduction	CodeCode Available	1
Self-Supervised Visual Representation Learning with Semantic Grouping	May 30, 2022	Contrastive LearningInstance Segmentation	CodeCode Available	1
A Closer Look at Self-Supervised Lightweight Vision Transformers	May 28, 2022	Contrastive Learningimage-classification	CodeCode Available	1
Multimodal Masked Autoencoders Learn Transferable Representations	May 27, 2022	Contrastive Learning	CodeCode Available	1
Target-aware Abstractive Related Work Generation with Contrastive Learning	May 26, 2022	Contrastive LearningTAG	CodeCode Available	1
Learning Dialogue Representations from Consecutive Utterances	May 26, 2022	Contrastive LearningConversational Question Answering	CodeCode Available	1
Cross-Architecture Self-supervised Video Representation Learning	May 26, 2022	Action RecognitionContrastive Learning	CodeCode Available	1
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning	May 26, 2022	Contrastive LearningQuantization	CodeCode Available	1
Contrastive Learning with Boosted Memorization	May 25, 2022	Contrastive LearningMemorization	CodeCode Available	1
Optimizing Test-Time Query Representations for Dense Retrieval	May 25, 2022	Contrastive LearningInformation Retrieval	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