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 2831–2840 of 6661 papers

Title	Date	Tasks	Status	Hype
Enhancing Text-based Knowledge Graph Completion with Zero-Shot Large Language Models: A Focus on Semantic Enhancement	Oct 12, 2023	Contrastive LearningData Augmentation	CodeCode Available	1
Splicing Up Your Predictions with RNA Contrastive Learning	Oct 12, 2023	Contrastive LearningProperty Prediction	CodeCode Available	0
X-Pose: Detecting Any Keypoints	Oct 12, 2023	2D Human Pose Estimation2D Pose Estimation	CodeCode Available	2
Rethinking Negative Pairs in Code Search	Oct 12, 2023	Code SearchContrastive Learning	CodeCode Available	1
SimCKP: Simple Contrastive Learning of Keyphrase Representations	Oct 12, 2023	Contrastive LearningKeyphrase Extraction	CodeCode Available	1
Visual Self-supervised Learning Scheme for Dense Prediction Tasks on X-ray Images	Oct 12, 2023	Contrastive LearningSelf-Supervised Learning	—Unverified	0
Language Models As Semantic Indexers	Oct 11, 2023	Contrastive LearningInformation Retrieval	CodeCode Available	1
IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-training	Oct 11, 2023	Contrastive LearningDescriptive	CodeCode Available	0
Heuristic Vision Pre-Training with Self-Supervised and Supervised Multi-Task Learning	Oct 11, 2023	Common Sense ReasoningContrastive Learning	—Unverified	0
The Solution for the CVPR2023 NICE Image Captioning Challenge	Oct 10, 2023	Contrastive LearningImage Captioning	—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