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Methods/Fractal Block

Fractal Block

Computer VisionIntroduced 20006 papers
Source Paper

Description

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 f_1(z)=conv(z)f\_{1}\left(z\right) = \text{conv}\left(z\right)f_1(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)] f\_{C+1}\left(z\right) = \left[\left(f\_{C}\circ{f\_{C}}\right)\left(z\right)\right] \oplus \left[\text{conv}\left(z\right)\right]f_C+1(z)=[(f_C∘f_C)(z)]⊕[conv(z)]

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

Papers Using This Method

Handwritten Bangla Character Recognition Using The State-of-Art Deep Convolutional Neural Networks2017-12-28CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior2017-10-30Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations2017-10-27SMASH: One-Shot Model Architecture Search through HyperNetworks2017-08-17Deep Convolutional Neural Network Design Patterns2016-11-02FractalNet: Ultra-Deep Neural Networks without Residuals2016-05-24