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 921–930 of 6661 papers

Title	Date	Tasks	Status	Hype
Test-Time Distribution Normalization for Contrastively Learned Vision-language Models	Feb 22, 2023	Contrastive Learning	CodeCode Available	1
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the Least	Feb 18, 2023	Contrastive LearningOpen-Ended Question Answering	CodeCode Available	1
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences	Feb 17, 2023	Action RecognitionContrastive Learning	CodeCode Available	1
Like a Good Nearest Neighbor: Practical Content Moderation and Text Classification	Feb 17, 2023	ClassificationContrastive Learning	CodeCode Available	1
CluCDD:Contrastive Dialogue Disentanglement via Clustering	Feb 16, 2023	ClusteringContrastive Learning	CodeCode Available	1
Multi-Source Contrastive Learning from Musical Audio	Feb 14, 2023	CoLAContrastive Learning	CodeCode Available	1
CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets	Feb 13, 2023	Contrastive LearningRepresentation Learning	CodeCode Available	1
Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition	Feb 13, 2023	Contrastive LearningFew-shot NER	CodeCode Available	1
Generalized Few-Shot Continual Learning with Contrastive Mixture of Adapters	Feb 12, 2023	Continual LearningContrastive Learning	CodeCode Available	1
LipLearner: Customizable Silent Speech Interactions on Mobile Devices	Feb 12, 2023	Contrastive LearningIncremental 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