Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Band Selection from Hyperspectral Images Using Attention-based
  Convolutional Neural Networks   This paper introduces new attention-based convolutional neural networks for
selecting bands from hyperspectral images. The proposed approach re-uses
convolutional activations at different depths, identifying the most informative
regions of the spectrum with the help of gating mechanisms. Our attention
techniques are modular and easy to implement, and they can be seamlessly
trained end-to-end using gradient descent. Our rigorous experiments showed that
deep models equipped with the attention mechanism deliver high-quality
classification, and repeatedly identify significant bands in the training data,
permitting the creation of refined and extremely compact sets that retain the
most meaningful features.

34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex