Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Delta-GAN-Encoder: Encoding Semantic Changes for Explicit Image Editing,
  using Few Synthetic Samples   Understating and controlling generative models' latent space is a complex
task.
  In this paper, we propose a novel method for learning to control any desired
attribute in a pre-trained GAN's latent space, for the purpose of editing
synthesized and real-world data samples accordingly.
  We perform Sim2Real learning, relying on minimal samples to achieve an
unlimited amount of continuous precise edits.
  We present an Autoencoder-based model that learns to encode the semantics of
changes between images as a basis for editing new samples later on, achieving
precise desired results - example shown in Fig. 1.
  While previous editing methods rely on a known structure of latent spaces
(e.g., linearity of some semantics in StyleGAN), our method inherently does not
require any structural constraints.
  We demonstrate our method in the domain of facial imagery: editing different
expressions, poses, and lighting attributes, achieving state-of-the-art
results.

37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio