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 1641–1650 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels	Apr 28, 2020	AllAtari Games 100k	CodeCode Available	1	5
Self-supervised Graph Neural Networks without explicit negative sampling	Mar 27, 2021	Contrastive LearningData Augmentation	CodeCode Available	1	5
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings	Mar 11, 2022	Contrastive LearningSemantic Textual Similarity	CodeCode Available	1	5
CSGCL: Community-Strength-Enhanced Graph Contrastive Learning	May 8, 2023	AttributeContrastive Learning	CodeCode Available	1	5
Image Quality Assessment using Contrastive Learning	Oct 25, 2021	Contrastive LearningImage Quality Assessment	CodeCode Available	1	5
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances	Jul 16, 2020	Anomaly DetectionContrastive Learning	CodeCode Available	1	5
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion	Nov 13, 2023	Contrastive LearningEEG	CodeCode Available	1	5
IMF: Interactive Multimodal Fusion Model for Link Prediction	Mar 20, 2023	Contrastive LearningKnowledge Graphs	CodeCode Available	1	5
i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning	Oct 17, 2020	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
Learning to Embed Time Series Patches Independently	Dec 27, 2023	Contrastive LearningRepresentation Learning	CodeCode Available	1	5

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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