Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Crafting a Toolchain for Image Restoration by Deep Reinforcement
  Learning   We investigate a novel approach for image restoration by reinforcement
learning. Unlike existing studies that mostly train a single large network for
a specialized task, we prepare a toolbox consisting of small-scale
convolutional networks of different complexities and specialized in different
tasks. Our method, RL-Restore, then learns a policy to select appropriate tools
from the toolbox to progressively restore the quality of a corrupted image. We
formulate a step-wise reward function proportional to how well the image is
restored at each step to learn the action policy. We also devise a joint
learning scheme to train the agent and tools for better performance in handling
uncertainty. In comparison to conventional human-designed networks, RL-Restore
is capable of restoring images corrupted with complex and unknown distortions
in a more parameter-efficient manner using the dynamically formed toolchain.

15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph