Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Supervised Intrinsic Image Decomposition   Intrinsic decomposition from a single image is a highly challenging task, due
to its inherent ambiguity and the scarcity of training data. In contrast to
traditional fully supervised learning approaches, in this paper we propose
learning intrinsic image decomposition by explaining the input image. Our
model, the Rendered Intrinsics Network (RIN), joins together an image
decomposition pipeline, which predicts reflectance, shape, and lighting
conditions given a single image, with a recombination function, a learned
shading model used to recompose the original input based off of intrinsic image
predictions. Our network can then use unsupervised reconstruction error as an
additional signal to improve its intermediate representations. This allows
large-scale unlabeled data to be useful during training, and also enables
transferring learned knowledge to images of unseen object categories, lighting
conditions, and shapes. Extensive experiments demonstrate that our method
performs well on both intrinsic image decomposition and knowledge transfer.

33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex