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AniWho : A Quick and Accurate Way to Classify Anime Character Faces in Images

2022-08-23 14:50:01
Martinus Grady Naftali, Jason Sebastian Sulistyawan, Kelvin Julian, Felix Indra Kurniadi
arXiv_CV
         
Abstract

This paper aims to dive more deeply into various models available, including; InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB7 using transfer learning, to classify Japanese animation-style character faces. This paper has shown that EfficientNet-B7 has the highest accuracy rate with 85.08\% top-1 Accuracy, followed by MobileNetV2, having a slightly less accurate result but with the benefits of much lower inference time and fewer number of required parameters. This paper also uses a few-shot learning framework, specifically Prototypical Networks, which produces decent results that can be used as an alternative to traditional transfer learning methods.

Abstract (translated)

URL

https://arxiv.org/abs/2208.11012

PDF

https://arxiv.org/pdf/2208.11012.pdf

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