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 4761–4770 of 6661 papers

Title	Date	Tasks	Status	Hype
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection	Jul 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning	Jul 4, 2022	AttributeContrastive Learning	CodeCode Available	1
Dynamic Contrastive Distillation for Image-Text Retrieval	Jul 4, 2022	Contrastive LearningGPU	—Unverified	0
Deep Contrastive One-Class Time Series Anomaly Detection	Jul 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval	Jul 2, 2022	Contrastive LearningCross-Modal Retrieval	—Unverified	0
GUIM -- General User and Item Embedding with Mixture of Representation in E-commerce	Jul 2, 2022	Contrastive LearningDemand Forecasting	—Unverified	0
Contrastive Data and Learning for Natural Language Processing	Jul 1, 2022	Contrastive LearningQuestion Answering	—Unverified	0
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection	Jul 1, 2022	Continual LearningContrastive Learning	—Unverified	0
RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction	Jul 1, 2022	Contrastive LearningData Augmentation	CodeCode Available	0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling	Jul 1, 2022	Contrastive LearningData Augmentation	—Unverified	0

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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