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 1001–1010 of 6661 papers

Title	Date	Tasks	Status	Hype
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning	Dec 8, 2022	Cancer ClassificationContrastive Learning	CodeCode Available	1
Neural Machine Translation with Contrastive Translation Memories	Dec 6, 2022	Contrastive LearningMachine Translation	CodeCode Available	1
Contrastive Domain Adaptation for Time-Series via Temporal Mixup	Dec 3, 2022	Contrastive LearningDomain Adaptation	CodeCode Available	1
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment	Dec 2, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series	Dec 2, 2022	ClusteringContrastive Learning	CodeCode Available	1
Hyperbolic Contrastive Learning for Visual Representations beyond Objects	Dec 1, 2022	Contrastive Learningimage-classification	CodeCode Available	1
CL4CTR: A Contrastive Learning Framework for CTR Prediction	Dec 1, 2022	Click-Through Rate PredictionContrastive Learning	CodeCode Available	1
Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs	Dec 1, 2022	Contrastive LearningOpen Vocabulary Semantic Segmentation	CodeCode Available	1
Normalized Contrastive Learning for Text-Video Retrieval	Nov 30, 2022	Contrastive LearningCross-Modal Retrieval	CodeCode Available	1
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation	Nov 26, 2022	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1

Show:10 25 50

← PrevPage 101 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