Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Some open questions on morphological operators and representations in
  the deep learning era   During recent years, the renaissance of neural networks as the major machine
learning paradigm and more specifically, the confirmation that deep learning
techniques provide state-of-the-art results for most of computer vision tasks
has been shaking up traditional research in image processing. The same can be
said for research in communities working on applied harmonic analysis,
information geometry, variational methods, etc. For many researchers, this is
viewed as an existential threat. On the one hand, research funding agencies
privilege mainstream approaches especially when these are unquestionably
suitable for solving real problems and for making progress on artificial
intelligence. On the other hand, successful publishing of research in our
communities is becoming almost exclusively based on a quantitative improvement
of the accuracy of any benchmark task. As most of my colleagues sharing this
research field, I am confronted with the dilemma of continuing to invest my
time and intellectual effort on mathematical morphology as my driving force for
research, or simply focussing on how to use deep learning and contributing to
it. The solution is not obvious to any of us since our research is not
fundamental, it is just oriented to solve challenging problems, which can be
more or less theoretical. Certainly, it would be foolish for anyone to claim
that deep learning is insignificant or to think that one's favourite image
processing domain is productive enough to ignore the state-of-the-art. I fully
understand that the labs and leading people in image processing communities
have been shifting their research to almost exclusively focus on deep learning
techniques. My own position is different: I do think there is room for progress
on mathematically grounded image processing branches, under the condition that
these are rethought in a broader sense from the deep learning paradigm. Indeed,
I firmly believe that the convergence between mathematical morphology and the
computation methods which gravitate around deep learning (fully connected
networks, convolutional neural networks, residual neural networks, recurrent
neural networks, etc.) is worthwhile. The goal of this talk is to discuss my
personal vision regarding these potential interactions. Without any pretension
of being exhaustive, I want to address it with a series of open questions,
covering a wide range of specificities of morphological operators and
representations, which could be tackled and revisited under the paradigm of
deep learning. An expected benefit of such convergence between morphology and
deep learning is a cross-fertilization of concepts and techniques between both
fields. In addition, I think the future answer to some of these questions can
provide some insight on understanding, interpreting and simplifying deep
learning networks.

7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas