Methods › Computer Vision › Image Model Blocks › Multiscale Dilated Convolution Block

Multiscale Dilated Convolution Block

5 papers tagged archive 2025-07-28

Introduced by Andrew Brock et al. in Neural Photo Editing with Introspective Adversarial Networks

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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 (MDC) block applies a single F×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.

PaperSourceSee Code · ajbrock/Neural-Photo-Editor

Papers archive 2025-07-28

5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

11 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Aspect-Based Sentiment Analysis1
Aspect-Based Sentiment Analysis (ABSA)1
Contrastive Learning1
Disaster Response1
Image Generation1
Image Segmentation1
Referring Expression Segmentation1
Segmentation1
Sentence1
Sentiment Analysis1
Video Object Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with Multiscale Dilated Convolution Block: 2016 to 2024, peak 1 1 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 1 paper 2020 2021: 1 paper 2021 2022: 0 papers 2022 2023: 1 paper 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (5 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Image Model Blocks

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