Machine Learning

A guide to convolution arithmetic for deep learning

This topic contains 0 replies, has 1 voice, and was last updated by  arXiv 11 months ago.


  • arXiv
    5 pts

    A guide to convolution arithmetic for deep learning

    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.

    A guide to convolution arithmetic for deep learning
    by Vincent Dumoulin, Francesco Visin
    https://arxiv.org/pdf/1603.07285v2.pdf

You must be logged in to reply to this topic.