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

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Showing 4571–4580 of 6661 papers

Title	Date	Tasks	Status
Balanced Supervised Contrastive Learning for Few-Shot Class-Incremental Learning	May 26, 2023	class-incremental learningClass Incremental Learning	—Unverified
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering	May 26, 2023	ClusteringContrastive Learning	CodeCode Available
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning	May 25, 2023	Activity RecognitionContrastive Learning	—Unverified
Enhancing the Ranking Context of Dense Retrieval Methods through Reciprocal Nearest Neighbors	May 25, 2023	Contrastive LearningReranking	CodeCode Available
Robust Category-Level 3D Pose Estimation from Synthetic Data	May 25, 2023	3D Pose Estimation3D Reconstruction	—Unverified
Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning	May 25, 2023	Contrastive LearningMultiple Instance Learning	CodeCode Available
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning	May 25, 2023	Contrastive LearningInformation Retrieval	—Unverified
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision	May 25, 2023	Anomaly DetectionContrastive Learning	—Unverified
TabGSL: Graph Structure Learning for Tabular Data Prediction	May 25, 2023	Contrastive LearningGraph Neural Network	—Unverified
Which Features are Learnt by Contrastive Learning? On the Role of Simplicity Bias in Class Collapse and Feature Suppression	May 25, 2023	Contrastive LearningRepresentation Learning	—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