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 1941–1950 of 6661 papers

Title	Date	Tasks	Status
Self-supervised Contrastive Learning for Audio-Visual Action Recognition	Apr 28, 2022	Action RecognitionContrastive Learning	—Unverified
ActiveMatch: End-to-end Semi-supervised Active Representation Learning	Oct 6, 2021	Active LearningContrastive Learning	—Unverified
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning	Nov 3, 2022	Contrastive LearningMolecular Property Prediction	—Unverified
Face-to-Face Contrastive Learning for Social Intelligence Question-Answering	Jul 29, 2022	Contrastive LearningGraph Neural Network	—Unverified
FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks	Jul 30, 2024	Contrastive Learning	—Unverified
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning	Oct 1, 2022	Contrastive LearningGraph Representation Learning	—Unverified
CoCGAN: Contrastive Learning for Adversarial Category Text Generation	Oct 1, 2022	Contrastive LearningText Generation	—Unverified
Feedback Reciprocal Graph Collaborative Filtering	Aug 5, 2024	Collaborative FilteringContrastive Learning	—Unverified
Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations	Aug 1, 2024	Contrastive LearningEEG	—Unverified
Divide and Contrast: Self-supervised Learning from Uncurated Data	May 17, 2021	ClusteringContrastive 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