Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On instabilities of deep learning in image reconstruction - Does AI come
  at a cost?   Deep learning, due to its unprecedented success in tasks such as image
classification, has emerged as a new tool in image reconstruction with
potential to change the field. In this paper we demonstrate a crucial
phenomenon: deep learning typically yields unstablemethods for image
reconstruction. The instabilities usually occur in several forms: (1) tiny,
almost undetectable perturbations, both in the image and sampling domain, may
result in severe artefacts in the reconstruction, (2) a small structural
change, for example a tumour, may not be captured in the reconstructed image
and (3) (a counterintuitive type of instability) more samples may yield poorer
performance. Our new stability test with algorithms and easy to use software
detects the instability phenomena. The test is aimed at researchers to test
their networks for instabilities and for government agencies, such as the Food
and Drug Administration (FDA), to secure safe use of deep learning methods.

21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
14
The categories of this paper are: math-ph