scholarly journals Nanophotonic inverse design with deep neural networks based on knowledge transfer using imbalanced datasets

2021 ◽  
Author(s):  
Cankun Qiu ◽  
Xia Wu ◽  
Zhi Luo ◽  
Huidong Yang ◽  
Guannan He ◽  
...  
Keyword(s):  
Neural Networks ◽  
Knowledge Transfer ◽  
Deep Neural Networks ◽  
Inverse Design ◽  
Imbalanced Datasets

Deep Neural Networks for Inverse Design of Nanophotonic Devices

2021 ◽  
pp. 1-1
Author(s):  
Keisuke Kojima ◽  
Mohammad H. Tahersima ◽  
Toshiaki Koike-Akino ◽  
Devesh K. Jha ◽  
Yingheng Tang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Inverse Design ◽  
Nanophotonic Devices

Deep Neural Network Inverse-Design for Long Wave Infrared Hyperspectral Imaging

2021 ◽  
Vol 35 (11) ◽  
pp. 1336-1337
Author(s):  
Clayton Fowler ◽  
Sensong An ◽  
Bowen Zheng ◽  
Hong Tang ◽  
Hang Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Hyperspectral Imaging ◽  
Electromagnetic Waves ◽  
Deep Neural Networks ◽  
Structural Parameters ◽  
Inverse Design ◽  
Metal Insulator ◽  
Complex Interactions ◽  
Metal Insulator Metal ◽  
Long Wave Infrared

This paper presents a deep learning approach for the inverse-design of metal-insulator-metal metasurfaces for hyperspectral imaging applications. Deep neural networks are able to compensate for the complex interactions between electromagnetic waves and metastructures to efficiently produce design solutions that would be difficult to obtain using other methods. Since electromagnetic spectra are sequential in nature, recurrent neural networks are especially suited for relating such spectra to structural parameters.

scholarly journals BBW: a batch balance wrapper for training deep neural networks on extremely imbalanced datasets with few minority samples

2021 ◽  
Author(s):  
Jingzhao Hu ◽  
Hao Zhang ◽  
Yang Liu ◽  
Richard Sutcliffe ◽  
Jun Feng
Keyword(s):  
Neural Networks ◽  
Learning Process ◽  
Deep Neural Networks ◽  
Imbalanced Data ◽  
Parameter Tuning ◽  
Classification Performance ◽  
Imbalanced Datasets ◽  
Sample Distribution ◽  
Network Layers ◽  
Additional Processing

AbstractIn recent years, Deep Neural Networks (DNNs) have achieved excellent performance on many tasks, but it is very difficult to train good models from imbalanced datasets. Creating balanced batches either by majority data down-sampling or by minority data up-sampling can solve the problem in certain cases. However, it may lead to learning process instability and overfitting. In this paper, we propose the Batch Balance Wrapper (BBW), a novel framework which can adapt a general DNN to be well trained from extremely imbalanced datasets with few minority samples. In BBW, two extra network layers are added to the start of a DNN. The layers prevent overfitting of minority samples and improve the expressiveness of the sample distribution of minority samples. Furthermore, Batch Balance (BB), a class-based sampling algorithm, is proposed to make sure the samples in each batch are always balanced during the learning process. We test BBW on three well-known extremely imbalanced datasets with few minority samples. The maximum imbalance ratio reaches 1167:1 with only 16 positive samples. Compared with existing approaches, BBW achieves better classification performance. In addition, BBW-wrapped DNNs are 16.39 times faster, relative to unwrapped DNNs. Moreover, BBW does not require data preprocessing or additional hyper-parameter tuning, operations that may require additional processing time. The experiments prove that BBW can be applied to common applications of extremely imbalanced data with few minority samples, such as the classification of EEG signals, medical images and so on.

scholarly journals Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures

ACS Photonics ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 1365-1369 ◽  
Author(s):  
Dianjing Liu ◽  
Yixuan Tan ◽  
Erfan Khoram ◽  
Zongfu Yu
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Inverse Design ◽  
Nanophotonic Structures

scholarly journals Face Photo-Sketch Synthesis via Knowledge Transfer

2019 ◽  
Author(s):  
Mingrui Zhu ◽  
Nannan Wang ◽  
Xinbo Gao ◽  
Jie Li ◽  
Zhifeng Li
Keyword(s):  
Neural Networks ◽  
Knowledge Transfer ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Training Data ◽  
Superior Performance ◽  
Teacher Networks ◽  
Recognition Ability ◽  
Small Set ◽  
Sketch Synthesis

Despite deep neural networks have demonstrated strong power in face photo-sketch synthesis task, their performance, however, are still limited by the lack of training data (photo-sketch pairs). Knowledge Transfer (KT), which aims at training a smaller and fast student network with the information learned from a larger and accurate teacher network, has attracted much attention recently due to its superior performance in the acceleration and compression of deep neural networks. This work has brought us great inspiration that we can train a relatively small student network on very few training data by transferring knowledge from a larger teacher model trained on enough training data for other tasks. Therefore, we propose a novel knowledge transfer framework to synthesize face photos from face sketches or synthesize face sketches from face photos. Particularly, we utilize two teacher networks trained on large amount of data in related task to learn the knowledge of face photos and face sketches separately and transfer them to two student networks simultaneously. In addition, the two student networks, one for photo ? sketch task and the other for sketch ? photo task, can transfer their knowledge mutually. With the proposed method, we can train our model which has superior performance using a small set of photo-sketch pairs. We validate the effectiveness of our method across several datasets. Quantitative and qualitative evaluations illustrate that our model outperforms other state-of-the-art methods in generating face sketches (or photos) with high visual quality and recognition ability.

scholarly journals Inverse Design for Silicon Photonics: From Iterative Optimization Algorithms to Deep Neural Networks

2021 ◽  
Vol 11 (9) ◽  
pp. 3822
Author(s):  
Simei Mao ◽  
Lirong Cheng ◽  
Caiyue Zhao ◽  
Faisal Nadeem Khan ◽  
Qian Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Silicon Photonics ◽  
Design Methodology ◽  
Degrees Of Freedom ◽  
Deep Neural Networks ◽  
Low Cost ◽  
Optimization Algorithms ◽  
Optimization Methods ◽  
Inverse Design ◽  
Iterative Optimization

Silicon photonics is a low-cost and versatile platform for various applications. For design of silicon photonic devices, the light-material interaction within its complex subwavelength geometry is difficult to investigate analytically and therefore numerical simulations are majorly adopted. To make the design process more time-efficient and to improve the device performance to its physical limits, various methods have been proposed over the past few years to manipulate the geometries of silicon platform for specific applications. In this review paper, we summarize the design methodologies for silicon photonics including iterative optimization algorithms and deep neural networks. In case of iterative optimization methods, we discuss them in different scenarios in the sequence of increased degrees of freedom: empirical structure, QR-code like structure and irregular structure. We also review inverse design approaches assisted by deep neural networks, which generate multiple devices with similar structure much faster than iterative optimization methods and are thus suitable in situations where piles of optical components are needed. Finally, the applications of inverse design methodology in optical neural networks are also discussed. This review intends to provide the readers with the suggestion for the most suitable design methodology for a specific scenario.

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