Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Invariant Representation for Unsupervised Image Restoration   Recently, cross domain transfer has been applied for unsupervised image
restoration tasks. However, directly applying existing frameworks would lead to
domain-shift problems in translated images due to lack of effective
supervision. Instead, we propose an unsupervised learning method that
explicitly learns invariant presentation from noisy data and reconstructs clear
observations. To do so, we introduce discrete disentangling representation and
adversarial domain adaption into general domain transfer framework, aided by
extra self-supervised modules including background and semantic consistency
constraints, learning robust representation under dual domain constraints, such
as feature and image domains. Experiments on synthetic and real noise removal
tasks show the proposed method achieves comparable performance with other
state-of-the-art supervised and unsupervised methods, while having faster and
stable convergence than other domain adaption methods.

20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat