Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SR2CNN: Zero-Shot Learning for Signal Recognition   Signal recognition is one of significant and challenging tasks in the signal
processing and communications field. It is often a common situation that
there's no training data accessible for some signal classes to perform a
recognition task. Hence, as widely-used in image processing field, zero-shot
learning (ZSL) is also very important for signal recognition. Unfortunately,
ZSL regarding this field has hardly been studied due to inexplicable signal
semantics. This paper proposes a ZSL framework, signal recognition and
reconstruction convolutional neural networks (SR2CNN), to address relevant
problems in this situation. The key idea behind SR2CNN is to learn the
representation of signal semantic feature space by introducing a proper
combination of cross entropy loss, center loss and autoencoder loss, as well as
adopting a suitable distance metric space such that semantic features have
greater minimal inter-class distance than maximal intra-class distance. The
proposed SR2CNN can discriminate signals even if no training data is available
for some signal class. Moreover, SR2CNN can gradually improve itself in the aid
of signal detection, because of constantly refined class center vectors in
semantic feature space. These merits are all verified by extensive experiments.

2
The categories of this paper are: math
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex