mlsanityData to 7 Oct 2026

DAD vision: opto-electronic co-designed computer vision with division adjoint method

arXiv 2211.03576 ↗First posted 4 Nov 2022cs.CV eess.IV physics.opticscited by 0

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Abstract (arXiv)

The miniaturization and mobility of computer vision systems are limited by the heavy computational burden and the size of optical lenses. Here, we propose to use a ultra-thin diffractive optical element to implement passive optical convolution. A division adjoint opto-electronic co-design method is also proposed. In our simulation experiments, the first few convolutional layers of the neural network can be replaced by optical convolution in a classification task on the CIFAR-10 dataset with no power consumption, while similar performance can be obtained.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 14 references that are in the map. First posted on arXiv 4 Nov 2022.

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References · 7

  1. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,548
  2. 2017Learning Efficient Convolutional Networks through Network Slimmingcited by 673
  3. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 520
  4. 2018All-Optical Machine Learning Using Diffractive Deep Neural Networkscited by 175
  5. 2014Phase Retrieval via Wirtinger Flow: Theory and Algorithmscited by 123
  6. 2019Deep Optics for Monocular Depth Estimation and 3D Object Detectioncited by 65
  7. 2019Deep Optics for Single-shot High-dynamic-range Imagingcited by 62

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