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 4671–4680 of 6661 papers

Title	Date	Tasks	Status	Hype
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond	Jul 30, 2022	Contrastive LearningDenoising	—Unverified	0
Face-to-Face Contrastive Learning for Social Intelligence Question-Answering	Jul 29, 2022	Contrastive LearningGraph Neural Network	—Unverified	0
Curriculum Learning for Data-Efficient Vision-Language Alignment	Jul 29, 2022	Contrastive LearningImage Retrieval	—Unverified	0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge	Jul 29, 2022	Active LearningContrastive Learning	—Unverified	0
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding	Jul 29, 2022	Contrastive LearningData Augmentation	—Unverified	0
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning	Jul 29, 2022	Contrastive LearningDeep Reinforcement Learning	CodeCode Available	1
Multimodal SuperCon: Classifier for Drivers of Deforestation in Indonesia	Jul 29, 2022	Contrastive Learning	CodeCode Available	1
Knowing Where and What: Unified Word Block Pretraining for Document Understanding	Jul 28, 2022	Contrastive Learningdocument understanding	CodeCode Available	0
Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning	Jul 28, 2022	Contrastive LearningMusic Recommendation	—Unverified	0
Time to augment self-supervised visual representation learning	Jul 27, 2022	Contrastive LearningObject	—Unverified	0

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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