English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Recent research efforts in optical computing have gravitated towardsdeveloping optical neural networks that aim to benefit from the processingspeed and parallelism of optics/photonics in machine learning applications.Among these endeavors, Diffractive Deep Neural Networks (D2NNs) harnesslight-matter interaction over a series of trainable surfaces, designed usingdeep learning, to compute a desired statistical inference task as the lightwaves propagate from the input plane to the output field-of-view. Although,earlier studies have demonstrated the generalization capability of diffractiveoptical networks to unseen data, achieving e.g., >98% image classificationaccuracy for handwritten digits, these previous designs are in generalsensitive to the spatial scaling, translation and rotation of the inputobjects. Here, we demonstrate a new training strategy for diffractive networksthat introduces input object translation, rotation and/or scaling during thetraining phase as uniformly distributed random variables to build resilience intheir blind inference performance against such object transformations. Thistraining strategy successfully guides the evolution of the diffractive opticalnetwork design towards a solution that is scale-, shift- androtation-invariant, which is especially important and useful for dynamicmachine vision applications in e.g., autonomous cars, in-vivo imaging ofbiomedical specimen, among others.

Scale-, shift- and rotation-invariant diffractive optical networks