Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Disentangled Unsupervised Image Translation via Restricted Information
  Flow   Unsupervised image-to-image translation methods aim to map images from one
domain into plausible examples from another domain while preserving structures
shared across two domains. In the many-to-many setting, an additional guidance
example from the target domain is used to determine domain-specific attributes
of the generated image. In the absence of attribute annotations, methods have
to infer which factors are specific to each domain from data during training.
Many state-of-art methods hard-code the desired shared-vs-specific split into
their architecture, severely restricting the scope of the problem. In this
paper, we propose a new method that does not rely on such inductive
architectural biases, and infers which attributes are domain-specific from data
by constraining information flow through the network using translation honesty
losses and a penalty on the capacity of domain-specific embedding. We show that
the proposed method achieves consistently high manipulation accuracy across two
synthetic and one natural dataset spanning a wide variety of domain-specific
and shared attributes.

20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn