Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Instance Separation Emerges from Inpainting   Deep neural networks trained to inpaint partially occluded images show a deep
understanding of image composition and have even been shown to remove objects
from images convincingly. In this work, we investigate how this implicit
knowledge of image composition can be leveraged for fully self-supervised
instance separation. We propose a measure for the independence of two image
regions given a fully self-supervised inpainting network and separate objects
by maximizing this independence. We evaluate our method on two microscopy image
datasets and show that it reaches similar segmentation performance to fully
supervised methods.

31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph