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

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Showing 691–700 of 6661 papers

Title	Date	Tasks	Status	Hype	Score
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics	Nov 26, 2021	Contrastive Learning	CodeCode Available	1	5
DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation	Dec 17, 2024	Contrastive LearningImage Segmentation	CodeCode Available	1	5
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot Filling	Oct 7, 2021	Contrastive Learningslot-filling	CodeCode Available	1	5
DVIS++: Improved Decoupled Framework for Universal Video Segmentation	Dec 20, 2023	Contrastive LearningDenoising	CodeCode Available	1	5
Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views	Oct 12, 2024	ClusteringContrastive Learning	CodeCode Available	1	5
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning	Aug 14, 2023	Contrastive LearningGenerative Adversarial Network	CodeCode Available	1	5
Continuous Learning for Android Malware Detection	Feb 8, 2023	Active LearningAndroid Malware Detection	CodeCode Available	1	5
Bridging Mini-Batch and Asymptotic Analysis in Contrastive Learning: From InfoNCE to Kernel-Based Losses	May 28, 2024	Contrastive LearningRepresentation Learning	CodeCode Available	1	5
A class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural network	Sep 21, 2023	Contrastive LearningData Augmentation	CodeCode Available	1	5
Deep Contrastive One-Class Time Series Anomaly Detection	Jul 4, 2022	Anomaly DetectionContrastive 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