In this section
Below is a list of all relevant publications authored by Robotics Forum members.
@inproceedings{Liu:2020:10.1007/978-3-030-58610-2_39,
author = {Liu, S and Lin, Z and Wang, Y and Jianming, Z and Perazzi, F and Johns, E},
doi = {10.1007/978-3-030-58610-2_39},
pages = {661--677},
publisher = {Springer Verlag},
title = {Shape adaptor: a learnable resizing module},
url = {http://dx.doi.org/10.1007/978-3-030-58610-2_39},
year = {2020}
}
TY - CPAPER
AB - 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.
AU - Liu,S
AU - Lin,Z
AU - Wang,Y
AU - Jianming,Z
AU - Perazzi,F
AU - Johns,E
DO - 10.1007/978-3-030-58610-2_39
EP - 677
PB - Springer Verlag
PY - 2020///
SN - 0302-9743
SP - 661
TI - Shape adaptor: a learnable resizing module
UR - http://dx.doi.org/10.1007/978-3-030-58610-2_39
UR - http://hdl.handle.net/10044/1/83622
ER -
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