Shape Adaptor: A Learnable Resizing Module

• URL: http://arxiv.org/abs/2008.00892v2
• Date: Mon, 10 Aug 2020 13:10:50 GMT
• Title: Shape Adaptor: A Learnable Resizing Module
• Authors: Shikun Liu, Zhe Lin, Yilin Wang, Jianming Zhang, Federico Perazzi, Edward Johns
• Abstract summary: We present a novel resizing module for neural networks: shape adaptor, a drop-in enhancement built on top of traditional resizing layers. Our implementation enables shape adaptors to be trained end-to-end without any additional supervision. We show the effectiveness of shape adaptors on two other applications: network compression and transfer learning.
• Score: 59.940372879848624
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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