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 961–970 of 6661 papers

Title	Date	Tasks	Status	Hype
Attributed Graph Clustering with Dual Redundancy Reduction	Apr 18, 2022	ClusteringContrastive Learning	CodeCode Available	1
KNN-BERT: Fine-Tuning Pre-Trained Models with KNN Classifier	Oct 6, 2021	Contrastive Learningtext-classification	CodeCode Available	1
Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures	Aug 18, 2022	AnatomyContrastive Learning	CodeCode Available	1
Co^2L: Contrastive Continual Learning	Jun 28, 2021	Continual LearningContrastive Learning	CodeCode Available	1
Co2L: Contrastive Continual Learning	Jan 1, 2021	Continual LearningContrastive Learning	CodeCode Available	1
Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Prediction	Feb 25, 2023	Contrastive LearningPrediction	CodeCode Available	1
CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning	Mar 30, 2021	Action LocalizationCoLA	CodeCode Available	1
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation	Aug 14, 2021	Contrastive LearningSelf-Supervised Learning	CodeCode Available	1
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking	Dec 14, 2020	Active LearningContrastive Learning	CodeCode Available	1
Self-supervised Spatial Reasoning on Multi-View Line Drawings	Apr 27, 2021	Binary ClassificationContrastive 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