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Found 6 papers, 0 papers with code
Classification and reconstruction of spatially overlapping phase images using diffractive optical networks
In addition to all-optical classification of overlapping phase objects, we also demonstrate the reconstruction of these phase images based on a shallow electronic neural network that uses the highly compressed output of the diffractive network as its input (with e. g., ~20-65 times less number of pixels) to rapidly reconstruct both of the phase images, despite their spatial overlap and related phase ambiguity.
Terahertz Pulse Shaping Using Diffractive Surfaces
Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics.
Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction.
Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks
We report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally-incoherent broadband source to all-optically perform a specific task learned using deep learning.
Response to Comment on "All-optical machine learning using diffractive deep neural networks"
In their Comment, Wei et al. (arXiv:1809. 08360v1 [cs. LG]) claim that our original interpretation of Diffractive Deep Neural Networks (D2NN) represent a mischaracterization of the system due to linearity and passivity.
All-Optical Machine Learning Using Diffractive Deep Neural Networks
We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively.
