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 2661–2670 of 6661 papers

Title	Date	Tasks	Status
C3L: Content Correlated Vision-Language Instruction Tuning Data Generation via Contrastive Learning	May 21, 2024	Contrastive Learning	—Unverified
TractoSCR: A Novel Supervised Contrastive Regression Framework for Prediction of Neurocognitive Measures Using Multi-Site Harmonized Diffusion MRI Tractography	Oct 13, 2022	Contrastive LearningDiffusion MRI	—Unverified
Contrastive Learning for Knowledge-Based Question Generation in Large Language Models	Sep 21, 2024	Contrastive LearningHallucination	—Unverified
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding	Jul 29, 2022	Contrastive LearningData Augmentation	—Unverified
Contrastive Pre-training for Zero-Shot Information Retrieval	Sep 29, 2021	Contrastive LearningFact Checking	—Unverified
Contrastive pretraining for semantic segmentation is robust to noisy positive pairs	Nov 24, 2022	Contrastive LearningSemantic Segmentation	—Unverified
C3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification	Aug 15, 2022	Contrastive LearningSelf-Supervised Learning	—Unverified
Contrastive Pre-training for Deep Session Data Understanding	Mar 5, 2024	Contrastive Learning	—Unverified
Contrastive Predictive Coding for Anomaly Detection	Jul 16, 2021	Anomaly DetectionAnomaly Segmentation	—Unverified
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning	May 22, 2023	Action RecognitionColorization	—Unverified

Show:10 25 50

← PrevPage 267 of 667Next →

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