  We present a novel resizing module for neural networks: shape adaptor, a
drop-in enhancement built on top of traditional resizing layers, such as
pooling, bilinear sampling, and strided convolution. Whilst traditional
resizing layers have fixed and deterministic reshaping factors, our module
allows for a learnable reshaping factor. Our implementation enables shape
adaptors to be trained end-to-end without any additional supervision, through
which network architectures can be optimised for each individual task, in a
fully automated way. We performed experiments across seven image classification
datasets, and results show that by simply using a set of our shape adaptors
instead of the original resizing layers, performance increases consistently
over human-designed networks, across all datasets. Additionally, we show the
effectiveness of shape adaptors on two other applications: network compression
and transfer learning. The source code is available at:
https://github.com/lorenmt/shape-adaptor.
