Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Signal Clustering with Class-independent Segmentation   Radar signals have been dramatically increasing in complexity, limiting the
source separation ability of traditional approaches. In this paper we propose a
Deep Learning-based clustering method, which encodes concurrent signals into
images, and, for the first time, tackles clustering with image segmentation.
Novel loss functions are introduced to optimize a Neural Network to separate
the input pulses into pure and non-fragmented clusters. Outperforming a variety
of baselines, the proposed approach is capable of clustering inputs directly
with a Neural Network, in an end-to-end fashion.

3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga