  One of the defining characteristics of human creativity is the ability to
make conceptual leaps, creating something surprising from typical knowledge. In
comparison, deep neural networks often struggle to handle cases outside of
their training data, which is especially problematic for problems with limited
training data. Approaches exist to transfer knowledge from problems with
sufficient data to those with insufficient data, but they tend to require
additional training or a domain-specific method of transfer. We present a new
approach, conceptual expansion, that serves as a general representation for
reusing existing trained models to derive new models without backpropagation.
We evaluate our approach on few-shot variations of two tasks: image
classification and image generation, and outperform standard transfer learning
approaches.
