Two studies on Convolutional Neural Networks sensibility to resolution

• Autor(a) principal: Abello, Antonio Augusto
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
• Texto Completo: https://www.teses.usp.br/teses/disponiveis/45/45134/tde-16122021-182010/
• topic: Deep learning; Deep learning; Face recognition; Reconhecimento facial; Super resolution; Super-resolução

Convolutional Neural Networks (CNNs) recently became the state-of-the-art for various Computer Vision tasks. However, for reasons not completely understood, they are very sensitive to low resolution images. This can be troublesome as real life applications such as automated driving or surveillance can not use high resolution sensors. In this work we perform two studies on this subject matter: on the first we empirically study the effect of resolution loss and image restoration algorithms on a Face Recognition model. On the second, we study the high frequency bias hypothesis, one of the current possible explanations for CNNs sensitivity. We are able to develop new techniques for image restoration that better deal with the low resolution recognition problem and advance the understanding of the high frequency bias in CNNs.