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 2021–2030 of 6661 papers

Title	Date	Tasks	Status
Dual Circle Contrastive Learning-Based Blind Image Super-Resolution	Mar 29, 2023	Contrastive LearningImage Super-Resolution	—Unverified
Few-Shot Classification with Contrastive Learning	Sep 17, 2022	ClassificationContrastive Learning	—Unverified
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU	May 5, 2024	Contrastive LearningData Augmentation	—Unverified
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning	Feb 14, 2022	Contrastive LearningFederated Learning	—Unverified
Dual Contrastive Learning for Spatio-temporal Representation	Jul 12, 2022	Contrastive LearningRepresentation Learning	—Unverified
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning	Feb 9, 2022	AllContrastive Learning	—Unverified
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning	Jan 3, 2024	ClassificationContrastive Learning	—Unverified
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders	Sep 14, 2023	Contrastive LearningKnowledge Distillation	—Unverified
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification	Dec 14, 2020	Contrastive LearningGraph Learning	—Unverified
Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations	Oct 13, 2023	Contrastive Learning	—Unverified

Show:10 25 50

← PrevPage 203 of 667Next →

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