Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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   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.

15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph