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 3221–3230 of 6661 papers

Title	Date	Tasks	Status	Hype
Class Instance Balanced Learning for Long-Tailed Classification	Jul 11, 2023	ClassificationContrastive Learning	—Unverified	0
CILF:Causality Inspired Learning Framework for Out-of-Distribution Vehicle Trajectory Prediction	Jul 11, 2023	Autonomous DrivingContrastive Learning	—Unverified	0
Mao-Zedong At SemEval-2023 Task 4: Label Represention Multi-Head Attention Model With Contrastive Learning-Enhanced Nearest Neighbor Mechanism For Multi-Label Text Classification	Jul 11, 2023	Contrastive LearningMulti Label Text Classification	CodeCode Available	0
Generative Contrastive Graph Learning for Recommendation	Jul 11, 2023	Collaborative FilteringContrastive Learning	CodeCode Available	1
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification	Jul 11, 2023	ClassificationContrastive Learning	—Unverified	0
mCLIP: Multilingual CLIP via Cross-lingual Transfer	Jul 10, 2023	Contrastive LearningCross-Lingual Transfer	CodeCode Available	1
Hate Speech Detection via Dual Contrastive Learning	Jul 10, 2023	Contrastive LearningHate Speech Detection	—Unverified	0
Weakly-supervised positional contrastive learning: application to cirrhosis classification	Jul 10, 2023	ClassificationContrastive Learning	CodeCode Available	1
Towards Cross-Table Masked Pretraining for Web Data Mining	Jul 10, 2023	Contrastive Learning	CodeCode Available	1
Improving Factuality of Abstractive Summarization via Contrastive Reward Learning	Jul 10, 2023	Abstractive Text SummarizationContrastive Learning	—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