Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Does Explainable Machine Learning Uncover the Black Box in Vision
  Applications?   Machine learning (ML) in general and deep learning (DL) in particular has
become an extremely popular tool in several vision applications (like object
detection, super resolution, segmentation, object tracking etc.). Almost in
parallel, the issue of explainability in ML (i.e. the ability to
explain/elaborate the way a trained ML model arrived at its decision) in vision
has also received fairly significant attention from various quarters. However,
we argue that the current philosophy behind explainable ML suffers from certain
limitations, and the resulting explanations may not meaningfully uncover black
box ML models. To elaborate our assertion, we first raise a few fundamental
questions which have not been adequately discussed in the corresponding
literature. We also provide perspectives on how explainablity in ML can benefit
by relying on more rigorous principles in the related areas.

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The categories of this paper are: atom-ph
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The categories of this paper are: stat
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The categories of this paper are: funct-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph