Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Part-based approximations for morphological operators using asymmetric
  auto-encoders   This paper addresses the issue of building a part-based representation of a
dataset of images. More precisely, we look for a non-negative, sparse
decomposition of the images on a reduced set of atoms, in order to unveil a
morphological and interpretable structure of the data. Additionally, we want
this decomposition to be computed online for any new sample that is not part of
the initial dataset. Therefore, our solution relies on a sparse, non-negative
auto-encoder where the encoder is deep (for accuracy) and the decoder shallow
(for interpretability). This method compares favorably to the state-of-the-art
online methods on two datasets (MNIST and Fashion MNIST), according to
classical metrics and to a new one we introduce, based on the invariance of the
representation to morphological dilation.

22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci