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 4441–4450 of 6661 papers

Title	Date	Tasks	Status	Hype
When and why vision-language models behave like bags-of-words, and what to do about it?	Oct 4, 2022	Contrastive LearningRetrieval	CodeCode Available	2
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions	Oct 4, 2022	Contrastive LearningGeneralization Bounds	—Unverified	0
Supervised Metric Learning to Rank for Retrieval via Contextual Similarity Optimization	Oct 4, 2022	Contrastive LearningImage Retrieval	CodeCode Available	1
Self-supervised Pre-training for Semantic Segmentation in an Indoor Scene	Oct 4, 2022	Contrastive LearningObject Rearrangement	—Unverified	0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers	Oct 4, 2022	Adversarial AttackContrastive Learning	—Unverified	0
CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning	Oct 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	0
Backdoor Attacks in the Supply Chain of Masked Image Modeling	Oct 4, 2022	Contrastive LearningSelf-Supervised Learning	—Unverified	0
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation	Oct 4, 2022	3D Semantic SegmentationContrastive Learning	CodeCode Available	1
ContraCLM: Contrastive Learning For Causal Language Model	Oct 3, 2022	Code GenerationCode Search	CodeCode Available	1
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training	Oct 3, 2022	3D Point Cloud ClassificationContrastive 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