HackerPapers

arXiv:1603.07285stat.ML

A guide to convolution arithmetic for deep learning

Vincent Dumoulin, Francesco Visin

Abstract

We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures. The guide clarifies the relationship between various properties (input shape, kernel shape, zero padding, strides and output shape) of convolutional, pooling and transposed convolutional layers, as well as the relationship between convolutional and transposed convolutional layers. Relationships are derived for various cases, and are illustrated in order to make them intuitive.

arXiv comments
Journal reference
DOI
Subjects
stat.MLMachine Learning (Statistics) cs.LGMachine Learning cs.NENeural and Evolutionary Computing

On Hacker News

Linked by 2 identifiable commenters in 2 comments across 2 threads, from Jun 2016 to Oct 2016. Found in 6 HN submissions from Mar 2016 to Aug 2022. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments2 threads

  1. Subpixel: A subpixel convolutional neural network implementation with Tensorflow 1 comment

    • And https://arxiv.org/abs/1603.07285 for the corresponding paper. Really clear and easy-to-understand explanation of some of the math.

      transcranialView on HNParent context

  2. What is the difference between deep learning and usual machine learning? 1 comment

    • I agree with you, it could surely expand this answer (I am the person who wrote this little answer). Since this was originally just an answer to a question I answered via email (if I remember correctly), I didn't go into too much depth regarding ConvNets, because I just wanted…

      rasbtView on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.