A Meta-Learning Approach to Predicting Performance and Data Requirements-Reference-Cited by-同舟云学术

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A Meta-Learning Approach to Predicting Performance and Data Requirements

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Jain Achin¹,Swaminathan Gurumurthy¹,Favaro Paolo¹,Yang Hao¹,Ravichandran Avinash¹,Harutyunyan Hrayr¹,Achille Alessandro¹,Dabeer Onkar¹,Schiele Bernt¹,Swaminathan Ashwin¹,Soatto Stefano¹

Affiliation:

1. AWS AI Labs

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10204097.pdf?arnumber=10204097

Reference47 articles.

1. icassava 2019 fine-grained visual categorization challenge;mwebaze;ArXiv Preprint,2019

2. Are we ready for Autonomous Driving? The KITTI Vision Bench-mark Suite;geiger;Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),0

3. Masked Autoencoders Are Scalable Vision Learners

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine Learning-Based Label Quality Assurance for Object Detection Projects in Requirements Engineering;Applied Sciences;2023-05-19

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