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 4391–4400 of 6661 papers

Title	Date	Tasks	Status	Hype
Closed-book Question Generation via Contrastive Learning	Oct 13, 2022	Contrastive LearningNatural Questions	CodeCode Available	0
RaP: Redundancy-aware Video-language Pre-training for Text-Video Retrieval	Oct 13, 2022	Contrastive LearningRetrieval	CodeCode Available	0
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RL	Oct 12, 2022	Contrastive LearningOut-of-Distribution Generalization	CodeCode Available	1
Language Agnostic Multilingual Information Retrieval with Contrastive Learning	Oct 12, 2022	Contrastive LearningCross-Lingual Transfer	CodeCode Available	0
Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles	Oct 12, 2022	Contrastive LearningFace Recognition	CodeCode Available	0
QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking	Oct 12, 2022	Contrastive LearningMultiple Object Tracking	CodeCode Available	2
Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning	Oct 12, 2022	Contrastive Learningcross-modal alignment	CodeCode Available	1
Self-supervised video pretraining yields robust and more human-aligned visual representations	Oct 12, 2022	Contrastive Learningobject-detection	—Unverified	0
Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning	Oct 12, 2022	Contrastive LearningForm	CodeCode Available	2
Self-Attention Message Passing for Contrastive Few-Shot Learning	Oct 12, 2022	Contrastive LearningFew-Shot 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