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 1861–1870 of 6661 papers

Title	Date	Tasks	Status	Hype
Unbiased Supervised Contrastive Learning	Nov 10, 2022	Contrastive Learning	CodeCode Available	1
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation	Mar 26, 2022	Contrastive Learningimage-classification	CodeCode Available	1
Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic Segmentation	Aug 12, 2022	Contrastive LearningDomain Adaptation	CodeCode Available	1
Understanding Collapse in Non-Contrastive Siamese Representation Learning	Sep 29, 2022	Continual LearningContrastive Learning	CodeCode Available	1
FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations	Oct 3, 2023	Contrastive Learning	CodeCode Available	1
Differentiable Data Augmentation for Contrastive Sentence Representation Learning	Oct 29, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Understanding Dimensional Collapse in Contrastive Self-supervised Learning	Oct 18, 2021	Contrastive LearningLearning Theory	CodeCode Available	1
Understanding Zero-Shot Adversarial Robustness for Large-Scale Models	Dec 14, 2022	Adversarial RobustnessContrastive Learning	CodeCode Available	1
Generalizable Implicit Hate Speech Detection Using Contrastive Learning	Oct 1, 2022	Contrastive LearningHate Speech Detection	CodeCode Available	1
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive Learning	Aug 15, 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