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 3931–3940 of 6661 papers

Title	Date	Tasks	Status	Hype
A Deep Behavior Path Matching Network for Click-Through Rate Prediction	Feb 1, 2023	Click-Through Rate PredictionContrastive Learning	—Unverified	0
A latent space for unsupervised MR image quality control via artifact assessment	Feb 1, 2023	Contrastive Learning	CodeCode Available	1
Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks	Jan 31, 2023	Contrastive LearningGraph Learning	CodeCode Available	1
Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning	Jan 31, 2023	Contrastive LearningNode Classification	CodeCode Available	0
NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks	Jan 31, 2023	Contrastive LearningEvolutionary Algorithms	CodeCode Available	0
ZhichunRoad at Amazon KDD Cup 2022: MultiTask Pre-Training for E-Commerce Product Search	Jan 31, 2023	Contrastive Learning	CodeCode Available	1
NoiseTransfer: Image Noise Generation with Contrastive Embeddings	Jan 31, 2023	Contrastive LearningDenoising	CodeCode Available	0
Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain Adaptation	Jan 31, 2023	ClusteringContrastive Learning	CodeCode Available	0
GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition	Jan 31, 2023	Contrastive LearningDomain Adaptation	CodeCode Available	1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks	Jan 31, 2023	Adversarial RobustnessAutonomous Driving	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