Optical Phase Dropout in Diffractive Deep Neural Network

TL;DR

This paper introduces optical phase dropout in diffractive deep neural networks to mitigate overfitting caused by limited sample sizes, improving classification accuracy through a novel physical dropout technique.

Contribution

It presents the first formulation of phase dropout in unitary space for diffractive neural networks, enhancing their generalization capabilities.

Findings

Optical phase dropout improves classification accuracy.

Synthetic masks effectively reduce overfitting.

Enhanced recognition performance demonstrated in experiments.

Abstract

Unitary learning is a backpropagation that serves to unitary weights update in deep complex-valued neural network with full connections, meeting a physical unitary prior in diffractive deep neural network ([DN]2). However, the square matrix property of unitary weights induces that the function signal has a limited dimension that could not generalize well. To address the overfitting problem that comes from the small samples loaded to [DN]2, an optical phase dropout trick is implemented. Phase dropout in unitary space that is evolved from a complex dropout and has a statistical inference is formulated for the first time. A synthetic mask recreated from random point apertures with random phase-shifting and its smothered modulation tailors the redundant links through incompletely sampling the input optical field at each diffractive layer. The physical features about the synthetic mask using different nonlinear activations are elucidated in detail. The equivalence between digital and diffractive model determines compound modulations that could successfully circumvent the nonlinear activations physically implemented in [DN]2. The numerical experiments verify the superiority of optical phase dropout in [DN]2 to enhance accuracy in 2D classification and recognition tasks-oriented.