Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset
  For Controlled Experiments   A growing number of approaches exist to generate explanations for image
classification. However, few of these approaches are subjected to human-subject
evaluations, partly because it is challenging to design controlled experiments
with natural image datasets, as they leave essential factors out of the
researcher's control. With our approach, researchers can describe their desired
dataset with only a few parameters. Based on these, our library generates
synthetic image data of two 3D abstract animals. The resulting data is suitable
for algorithmic as well as human-subject evaluations. Our user study results
demonstrate that our method can create biases predictive enough for a
classifier and subtle enough to be noticeable only to every second participant
inspecting the data visually. Our approach significantly lowers the barrier for
conducting human subject evaluations, thereby facilitating more rigorous
investigations into interpretable machine learning. For our library and
datasets see, https://github.com/mschuessler/two4two/

10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int