Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Novel Weight-Shared Multi-Stage CNN for Scale Robustness   Convolutional neural networks (CNNs) have demonstrated remarkable results in
image classification for benchmark tasks and practical applications. The CNNs
with deeper architectures have achieved even higher performance recently thanks
to their robustness to the parallel shift of objects in images as well as their
numerous parameters and the resulting high expression ability. However, CNNs
have a limited robustness to other geometric transformations such as scaling
and rotation. This limits the performance improvement of the deep CNNs, but
there is no established solution. This study focuses on scale transformation
and proposes a network architecture called the weight-shared multi-stage
network (WSMS-Net), which consists of multiple stages of CNNs. The proposed
WSMS-Net is easily combined with existing deep CNNs such as ResNet and DenseNet
and enables them to acquire robustness to object scaling. Experimental results
on the CIFAR-10, CIFAR-100, and ImageNet datasets demonstrate that existing
deep CNNs combined with the proposed WSMS-Net achieve higher accuracies for
image classification tasks with only a minor increase in the number of
parameters and computation time.

24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph