“AniWho: A Quick and Accurate Way to Classify Anime Character Faces in Images”, Martinus Grady Naftali, Jason Sebastian Sulistyawan, Kelvin Julian, Felix Indra Kurniadi2022-08-23 (Danbooru AI, neural net)⁠:

This paper aims to dive more deeply into various models available, including InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNet-B7, 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.