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 2741–2750 of 6661 papers

Title	Date	Tasks	Status
An LLM-Empowered Low-Resolution Vision System for On-Device Human Behavior Understanding	May 3, 2025	Contrastive Learning	—Unverified
Contrastive Learning to Improve Retrieval for Real-world Fact Checking	Oct 7, 2024	Contrastive LearningFact Checking	—Unverified
Contrastive Learning to Fine-Tune Feature Extraction Models for the Visual Cortex	Oct 8, 2024	Contrastive LearningDimensionality Reduction	—Unverified
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification	Mar 1, 2022	ClusteringContrastive Learning	—Unverified
Contrastive Learning Through Time	Oct 12, 2021	Contrastive LearningRepresentation Learning	—Unverified
Contrastive Learning Subspace for Text Clustering	Aug 26, 2024	ClusteringContrastive Learning	—Unverified
Bridge the Gap between Language models and Tabular Understanding	Feb 16, 2023	Contrastive LearningLanguage Modeling	—Unverified
ANL: Anti-Noise Learning for Cross-Domain Person Re-Identification	Dec 27, 2020	ClusteringContrastive Learning	—Unverified
A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation	May 25, 2024	Contrastive LearningImage Segmentation	—Unverified
Contrastive Learning Relies More on Spatial Inductive Bias Than Supervised Learning: An Empirical Study	Jan 1, 2023	Contrastive LearningInductive Bias	—Unverified

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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