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Fractal Block

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.

A Fractal Block is an image model block that utilizes an expansion rule that yields a structural layout of truncated fractals. For the base case where f₁(z) = conv(z) is a convolutional layer, we then have recursive fractals of the form:

f_(C+1)(z) = [(f_C∘f_C)(z)] ⊕[conv(z)]

Where C is the number of columns. For the join layer (green in Figure), we use the element-wise mean rather than concatenation or addition.

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 Fractal Block: 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

Image Model Blocks

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