Multiscale Dilated Convolution Block
Multiscale Dilated Convolution Block
A **Multiscale Dilated Convolution Block** is an Inception-style convolutional block motivated by the ideas that image features naturally occur at multiple scales, that a network’s expressivity is proportional to the range of functions it can represent divided by its total number of parameters, and by the desire to efficiently expand a network’s receptive field. The Multiscale [Dilated Convolution](https://paperswithcode.com/method/dilated-convolution) (MDC) block applies a single $F\times{F}$ filter at multiple dilation factors, then performs a weighted elementwise sum of each dilated filter’s output, allowing the network to simultaneously learn a set of features and the relevant scales at which those features occur with a minimal increase in parameters. This also rapidly expands the network’s receptive field without requiring an increase in depth or the number of parameters.
Neural Photo Editing with Introspective Adversarial Networks
https://paperswithcode.com/paper/neural-photo-editing-with-introspective
Neural Photo Editing with Introspective Adversarial Networks