Towards a visualization of deep neural networks for rough line images
| Autor: | Serap A. Savari, Narendra Chaudhary |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Creative visualization
Artificial neural network business.industry Computer science media_common.quotation_subject Deep learning Pattern recognition Convolutional neural network Edge detection Visualization Enhanced Data Rates for GSM Evolution Artificial intelligence Noise (video) business media_common |
| Zdroj: | 35th European Mask and Lithography Conference (EMLC 2019). |
| DOI: | 10.1117/12.2535667 |
| Popis: | Low dose scanning electron microscope (SEM) images are an attractive option to estimate the roughness of nanos- tructures. We recently proposed two deep convolutional neural network (CNN) architectures named “LineNet” to simultaneously perform denoising and edge estimation on rough line SEM images. In this paper we consider multiple visualization tools to improve our understanding of LineNet1; one of these techniques is new to the visualization of denoising CNNs. We use the resulting insights from these visualizations to motivate a study of two variations of LineNet1 with fewer neural network layers. Furthermore, although in classification CNNs edge detection is commonly believed to happen early in the network, the visualization techniques suggest that important aspects of edge detection in LineNet1 occur late in the network. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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