Coherence Awareness in Diffractive Neural Networks

• URL: http://arxiv.org/abs/2408.06681v1
• Date: Tue, 13 Aug 2024 07:19:40 GMT
• Title: Coherence Awareness in Diffractive Neural Networks
• Authors: Matan Kleiner, Lior Michaeli, Tomer Michaeli,
• Abstract summary: We show that in diffractive networks the degree of spatial coherence has a dramatic effect. In particular, we show that when the spatial coherence length on the object is comparable to the minimal feature size preserved by the optical system, neither the incoherent nor the coherent extremes serve as acceptable approximations.
• Score: 21.264497139730473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffractive neural networks hold great promise for applications requiring intensive computational processing. Considerable attention has focused on diffractive networks for either spatially coherent or spatially incoherent illumination. Here we illustrate that, as opposed to imaging systems, in diffractive networks the degree of spatial coherence has a dramatic effect. In particular, we show that when the spatial coherence length on the object is comparable to the minimal feature size preserved by the optical system, neither the incoherent nor the coherent extremes serve as acceptable approximations. Importantly, this situation is inherent to many settings involving active illumination, including reflected light microscopy, autonomous vehicles and smartphones. Following this observation, we propose a general framework for training diffractive networks for any specified degree of spatial and temporal coherence, supporting all types of linear and nonlinear layers. Using our method, we numerically optimize networks for image classification, and thoroughly investigate their performance dependence on the illumination coherence properties. We further introduce the concept of coherence-blind networks, which have enhanced resilience to changes in illumination conditions. Our findings serve as a steppingstone toward adopting all-optical neural networks in real-world applications, leveraging nothing but natural light.

Related papers

• Hallmarks of Optimization Trajectories in Neural Networks: Directional Exploration and Redundancy [75.15685966213832]
  We analyze the rich directional structure of optimization trajectories represented by their pointwise parameters. We show that training only scalar batchnorm parameters some while into training matches the performance of training the entire network.
  arXiv  Detail & Related papers  (2024-03-12T07:32:47Z)
• Time-lapse image classification using a diffractive neural network [0.0]
  We show for the first time a time-lapse image classification scheme using a diffractive network. We show a blind testing accuracy of 62.03% on the optical classification of objects from the CIFAR-10 dataset. This constitutes the highest inference accuracy achieved so far using a single diffractive network.
  arXiv  Detail & Related papers  (2022-08-23T08:16:30Z)
• USegScene: Unsupervised Learning of Depth, Optical Flow and Ego-Motion with Semantic Guidance and Coupled Networks [31.600708674008384]
  USegScene is a framework for semantically guided unsupervised learning of depth, optical flow and ego-motion estimation for stereo camera images. We present results on the popular KITTI dataset and show that our approach outperforms other methods by a large margin.
  arXiv  Detail & Related papers  (2022-07-15T13:25:47Z)
• Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks [88.77951448313486]
  We present a new approach for model acceleration by exploiting spatial sparsity in visual data. We propose a dynamic token sparsification framework to prune redundant tokens. We extend our method to hierarchical models including CNNs and hierarchical vision Transformers.
  arXiv  Detail & Related papers  (2022-07-04T17:00:51Z)
• RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images [82.1679766706423]
  Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. We propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs. Our proposed RRNet outperforms the existing state-of-the-art SOD competitors both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2021-10-27T07:18:32Z)
• Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo [105.05232615226602]
  We leverage differentiable neural architecture search (NAS) strategy to find uncalibrated PS architecture automatically. Experiments on the DiLiGenT dataset show that the automatically searched neural architectures performance compares favorably with the state-of-the-art uncalibrated PS methods.
  arXiv  Detail & Related papers  (2021-10-11T21:22:17Z)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)
• Scale-, shift- and rotation-invariant diffractive optical networks [0.0]
  Diffractive Deep Neural Networks (D2NNs) harness light-matter interaction over a series of trainable surfaces to compute a desired statistical inference task. Here, we demonstrate a new training strategy for diffractive networks that introduces input object translation, rotation and/or scaling during the training phase. This training strategy successfully guides the evolution of the diffractive optical network design towards a solution that is scale-, shift- and rotation-invariant.
  arXiv  Detail & Related papers  (2020-10-24T02:18:39Z)
• A Parallel Down-Up Fusion Network for Salient Object Detection in Optical Remote Sensing Images [82.87122287748791]
  We propose a novel Parallel Down-up Fusion network (PDF-Net) for salient object detection in optical remote sensing images (RSIs) It takes full advantage of the in-path low- and high-level features and cross-path multi-resolution features to distinguish diversely scaled salient objects and suppress the cluttered backgrounds. Experiments on the ORSSD dataset demonstrate that the proposed network is superior to the state-of-the-art approaches both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2020-10-02T05:27:57Z)
• Multi-objective and categorical global optimization of photonic structures based on ResNet generative neural networks [0.0]
  A residual network scheme enables GLOnets to evolve from a deep architecture to a shallow network that generates a narrow distribution of globally optimal devices. We show that GLOnets can find the global optimum with orders of magnitude faster speeds compared to conventional algorithms. Results indicate that advanced concepts in deep learning can push the capabilities of inverse design algorithms for photonics.
  arXiv  Detail & Related papers  (2020-07-20T06:50:53Z)
• Compatible Learning for Deep Photonic Neural Network [0.0]
  Photonic neural network has a significant potential for prediction-oriented tasks. We develop a compatible learning protocol in complex space, of which nonlinear activation could be selected efficiently.
  arXiv  Detail & Related papers  (2020-03-14T13:21:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.