  Visual cognition of primates is superior to that of artificial neural
networks in its ability to 'envision' a visual object, even a newly-introduced
one, in different attributes including pose, position, color, texture, etc. To
aid neural networks to envision objects with different attributes, we propose a
family of objective functions, expressed on groups of examples, as a novel
learning framework that we term Group-Supervised Learning (GSL). GSL allows us
to decompose inputs into a disentangled representation with swappable
components, that can be recombined to synthesize new samples. For instance,
images of red boats & blue cars can be decomposed and recombined to synthesize
novel images of red cars. We propose an implementation based on auto-encoder,
termed group-supervised zero-shot synthesis network (GZS-Net) trained with our
learning framework, that can produce a high-quality red car even if no such
example is witnessed during training. We test our model and learning framework
on existing benchmarks, in addition to anew dataset that we open-source. We
qualitatively and quantitatively demonstrate that GZS-Net trained with GSL
outperforms state-of-the-art methods.
