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Translation-invariant optical neural network for image classification.

Hoda Sadeghzadeh1, Somayyeh Koohi2

  • 1Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

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Summary
This summary is machine-generated.

This study introduces Trans-ONN, an all-optical Convolutional Neural Network (CNN) designed for accurate image classification despite translations. The novel optical motion pooling and global average pooling (GAP) layers enable robust performance on shifted images.

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Area of Science:

  • Optics and Photonics
  • Artificial Intelligence
  • Computer Vision

Background:

  • All-optical Convolutional Neural Networks (CNNs) face challenges with component misalignment and input image translation.
  • These issues significantly degrade classification performance in practical applications.

Purpose of the Study:

  • To propose a novel free-space all-optical CNN, Trans-ONN, capable of accurately classifying translated images.
  • To enhance translation invariance in optical CNNs for improved real-world applicability.

Main Methods:

  • Introduced an optical motion pooling layer with Fourier plane masks to achieve translation invariance.
  • Utilized global average pooling (GAP) instead of fully connected layers to further enhance translation invariance.
  • Evaluated Trans-ONN on Kaggle Cats and Dogs, CIFAR-10, and MNIST datasets with varying pixel shifts.

Main Results:

  • Trans-ONN with vertical and horizontal masks plus GAP demonstrated superior translation invariance for horizontal/vertical shifts (up to 50 pixels).
  • Trans-ONN with a diagonal mask plus GAP achieved the highest accuracy for diagonal shifts (>30 pixels).
  • The proposed networks successfully classified translated images not encountered during training.

Conclusions:

  • The Trans-ONN architecture effectively addresses translation issues in all-optical CNNs.
  • The combination of optical motion pooling and GAP provides a robust solution for translation-invariant image classification.
  • This work advances the development of practical and reliable all-optical computing systems.