  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 the 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, under the linear circumstance, we theoretically prove that
our algorithm derives the principal components as PCA does. Codes can be found
in https://github.com/LynnHo/EigenGAN-Tensorflow.
