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 4121–4130 of 6661 papers

Title	Date	Tasks	Status	Hype
Neural Machine Translation with Contrastive Translation Memories	Dec 6, 2022	Contrastive LearningMachine Translation	CodeCode Available	1
Semi-Supervised Object Detection with Object-wise Contrastive Learning and Regression Uncertainty	Dec 6, 2022	ClassificationContrastive Learning	—Unverified	0
InternVideo: General Video Foundation Models via Generative and Discriminative Learning	Dec 6, 2022	Action ClassificationAction Recognition	CodeCode Available	4
Location-Aware Self-Supervised Transformers for Semantic Segmentation	Dec 5, 2022	Contrastive Learningimage-classification	—Unverified	0
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation	Dec 5, 2022	Contrastive LearningCross-Domain Few-Shot	—Unverified	0
Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning	Dec 4, 2022	Contrastive LearningTransfer Learning	—Unverified	0
Contrastive Domain Adaptation for Time-Series via Temporal Mixup	Dec 3, 2022	Contrastive LearningDomain Adaptation	CodeCode Available	1
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series	Dec 2, 2022	ClusteringContrastive Learning	CodeCode Available	1
3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation	Dec 2, 2022	3D GenerationContrastive Learning	—Unverified	0
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment	Dec 2, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1

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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