  The generative adversarial network (GAN) exhibits great superiority in the
face attribute synthesis task. However, existing methods have very limited
effects on the expansion of new attributes. To overcome the limitations of a
single network in new attribute synthesis, a continuous learning method for
face attribute synthesis is proposed in this work. First, the feature vector of
the input image is extracted and attribute direction regression is performed in
the feature space to obtain the axes of different attributes. The feature
vector is then linearly guided along the axis so that images with target
attributes can be synthesized by the decoder. Finally, to make the network
capable of continuous learning, the orthogonal direction modification module is
used to extend the newly-added attributes. Experimental results show that the
proposed method can endow a single network with the ability to learn attributes
continuously, and, as compared to those produced by the current
state-of-the-art methods, the synthetic attributes have higher accuracy.
