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7 CONCLUSION AND FUTURE
SCOPE
In this work, we improved the capabilities of single
image models to accommodate multiple images. This
is possible with simple assumptions of similarities in
underlying content and a modified discriminator ar-
chitecture and objective function. When we consider
two face images that are roughly aligned, but differ
in other aspects like texture, color and light intensi-
ties, our method involves learning a distribution of the
patches that appear from the natural composition of
the input images. The idea extends to multiple im-
ages, assuming that the images are roughly aligned
and the images share similar underlying content lay-
outs. Our method generates diverse set of hundreds
of data samples by training on just two input images.
Future work can focus on improving control over the
style at global and local level.
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