A Comparison for Anti-noise Robustness of Deep Learning Classification Methods on a Tiny Object Image Dataset: from Convolutional Neural Network to Visual Transformer and Performer

• URL: http://arxiv.org/abs/2106.01927v1
• Date: Thu, 3 Jun 2021 15:28:17 GMT
• Title: A Comparison for Anti-noise Robustness of Deep Learning Classification Methods on a Tiny Object Image Dataset: from Convolutional Neural Network to Visual Transformer and Performer
• Authors: Ao Chen, Chen Li, Haoyuan Chen, Hechen Yang, Peng Zhao, Weiming Hu, Wanli Liu, Shuojia Zou, and Marcin Grzegorzek
• Abstract summary: We first briefly review the development of Convolutional Neural Network and Visual Transformer in deep learning. We then use various models of Convolutional Neural Network and Visual Transformer to conduct a series of experiments on the image dataset of tiny objects. We discuss the problems in the classification of tiny objects and make a prospect for the classification of tiny objects in the future.
• Score: 27.023667473278266
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image classification has achieved unprecedented advance with the the rapid development of deep learning. However, the classification of tiny object images is still not well investigated. In this paper, we first briefly review the development of Convolutional Neural Network and Visual Transformer in deep learning, and introduce the sources and development of conventional noises and adversarial attacks. Then we use various models of Convolutional Neural Network and Visual Transformer to conduct a series of experiments on the image dataset of tiny objects (sperms and impurities), and compare various evaluation metrics in the experimental results to obtain a model with stable performance. Finally, we discuss the problems in the classification of tiny objects and make a prospect for the classification of tiny objects in the future.

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