Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generative Counterfactual Introspection for Explainable Deep Learning   In this work, we propose an introspection technique for deep neural networks
that relies on a generative model to instigate salient editing of the input
image for model interpretation. Such modification provides the fundamental
interventional operation that allows us to obtain answers to counterfactual
inquiries, i.e., what meaningful change can be made to the input image in order
to alter the prediction. We demonstrate how to reveal interesting properties of
the given classifiers by utilizing the proposed introspection approach on both
the MNIST and the CelebA dataset.

36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
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The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an