Methods › Computer Vision › Convolutional Neural Networks › FractalNet

FractalNet

6 papers tagged archive 2025-07-28

Introduced by Gustav Larsson et al. in FractalNet: Ultra-Deep Neural Networks without Residuals

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

FractalNet is a type of convolutional neural network that eschews residual connections in favour of a "fractal" design. They involve repeated application of a simple expansion rule to generate deep networks whose structural layouts are precisely truncated fractals. These networks contain interacting subpaths of different lengths, but do not include any pass-through or residual connections; every internal signal is transformed by a filter and nonlinearity before being seen by subsequent layers.

PaperSourceSee Code · osmr/imgclsmob

Papers archive 2025-07-28

6 shown of 6, 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

6 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
Deep Learning1
Image Classification1
Neural Architecture Search1
Object Recognition1
Translation1
model1

Usage over time archive 2025-07-28

Papers per year tagged with FractalNet: 2016 to 2017, peak 4 4 0 2016: 2 papers 2016 2017: 4 papers 2017
Papers per year the archive tags with this method, by the paper's archive date (6 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

Convolutional Neural Networks

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