Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Understanding and Improving Convolutional Neural Networks via
  Concatenated Rectified Linear Units   Recently, convolutional neural networks (CNNs) have been used as a powerful
tool to solve many problems of machine learning and computer vision. In this
paper, we aim to provide insight on the property of convolutional neural
networks, as well as a generic method to improve the performance of many CNN
architectures. Specifically, we first examine existing CNN models and observe
an intriguing property that the filters in the lower layers form pairs (i.e.,
filters with opposite phase). Inspired by our observation, we propose a novel,
simple yet effective activation scheme called concatenated ReLU (CRelu) and
theoretically analyze its reconstruction property in CNNs. We integrate CRelu
into several state-of-the-art CNN architectures and demonstrate improvement in
their recognition performance on CIFAR-10/100 and ImageNet datasets with fewer
trainable parameters. Our results suggest that better understanding of the
properties of CNNs can lead to significant performance improvement with a
simple modification.

18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph