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 4131–4140 of 6661 papers

Title	Date	Tasks	Status
Online Continual Learning with Contrastive Vision Transformer	Jul 24, 2022	Continual LearningContrastive Learning	—Unverified
Online Object Representations with Contrastive Learning	Jun 10, 2019	Contrastive LearningObject	—Unverified
Online pre-training with long-form videos	Aug 28, 2024	Action RecognitionContrastive Learning	—Unverified
On Mutual Information in Contrastive Learning for Visual Representations	May 27, 2020	Contrastive LearningGeneral Classification	—Unverified
On Negative Sampling for Audio-Visual Contrastive Learning from Movies	Apr 29, 2022	Action RecognitionAudio Classification	—Unverified
On Negative Sampling for Contrastive Audio-Text Retrieval	Nov 8, 2022	Audio to Text RetrievalContrastive Learning	—Unverified
On Self-Supervised Image Representations for GAN Evaluation	Jan 1, 2021	Contrastive LearningGeneral Classification	—Unverified
On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval	Nov 1, 2023	Contrastive LearningEntity Retrieval	—Unverified
On the Adversarial Robustness of Graph Contrastive Learning Methods	Nov 29, 2023	Adversarial RobustnessContrastive Learning	—Unverified
On the Comparison between Multi-modal and Single-modal Contrastive Learning	Nov 5, 2024	Contrastive LearningLearning Theory	—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