Abstract. Perceiving a visual concept as a mixture of learned ones is
natural for humans, aiding them to grasp new concepts and strengthening old ones. For all their power and recent success, deep convolutional
networks do not have this ability. Inspired by recent work on universal
representations for neural networks, we propose a simple emulation of
this mechanism by purposing batch normalization layers to discriminate
visual classes, and formulating a way to combine them to solve new tasks.
We show that this can be applied for 2-way few-shot learning where we
obtain between 4% and 17% better accuracy compared to straightforward full fine-tuning, and demonstrate that it can also be extended to
the orthogonal application of style transfer.