  Recent studies on Generative Adversarial Network (GAN) reveal that different
layers of a generative CNN hold different semantics of the synthesized images.
However, few GAN models have explicit dimensions to control the semantic
attributes represented in a specific layer. This paper proposes EigenGAN which
is able to unsupervisedly mine interpretable and controllable dimensions from
different generator layers. Specifically, EigenGAN embeds one linear subspace
with orthogonal basis into each generator layer. Via generative adversarial
training to learn a target distribution, these layer-wise subspaces
automatically discover a set of "eigen-dimensions" at each layer corresponding
to a set of semantic attributes or interpretable variations. By traversing the
coefficient of a specific eigen-dimension, the generator can produce samples
with continuous changes corresponding to a specific semantic attribute. Taking
the human face for example, EigenGAN can discover controllable dimensions for
high-level concepts such as pose and gender in the subspace of deep layers, as
well as low-level concepts such as hue and color in the subspace of shallow
layers. Moreover, in the linear case, we theoretically prove that our algorithm
derives the principal components as PCA does. Codes can be found in
https://github.com/LynnHo/EigenGAN-Tensorflow.
