Increasing the Accuracy of a Neural Network Using Frequency Selective Mesh-to-Grid Resampling-Reference-Cited by-同舟云学术

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Increasing the Accuracy of a Neural Network Using Frequency Selective Mesh-to-Grid Resampling

Published:2022-05-28 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Symposium on Circuits and Systems (ISCAS)
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Author:

Spruck Andreas¹,Heimann Viktoria¹,Kaup Andre¹

Affiliation:

1. Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg Cauerstr. 7,Erlangen,Germany,91058

Funder

Ministry of Education

Deutsche Forschungsgemeinschaft

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9937201/9937203/09937735.pdf?arnumber=9937735

Reference22 articles.

1. Data augmentation for improving deep learning in image classification problem

2. A Visual Vocabulary for Flower Classification

3. Cats and dogs

4. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

5. Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting

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. Jointly Resampling and Reconstructing Corrupted Images for Image Classification using Frequency-Selective Mesh-to-Grid Resampling;2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP);2022-09-26

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