mlsanityData to 14 Oct 2026

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

arXiv 2208.11012 ↗First posted 23 Aug 2022cs.CV cs.LGcited by 4

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Abstract (arXiv)

In order to classify Japanese animation-style character faces, this paper attempts to delve further into the many models currently available, including InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNet, employing transfer learning. This paper demonstrates that EfficientNet-B7, which achieves a top-1 accuracy of 85.08%, has the highest accuracy rate. MobileNetV2, which achieves a less accurate result with a top-1 accuracy of 81.92%, benefits from a significantly faster inference time and fewer required parameters. However, from the experiment, MobileNet-V2 is prone to overfitting; EfficienNet-B0 fixed the overfitting issue but with a cost of a little slower in inference time than MobileNet-V2 but a little more accurate result, top-1 accuracy of 83.46%. This paper also uses a few-shot learning architecture called Prototypical Networks, which offers an adequate substitute for conventional transfer learning techniques.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 35 references that are in the map. First posted on arXiv 23 Aug 2022.

Builds on

20 of 35 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  5. 2017Squeeze-and-Excitation Networkscited by 4,789
  6. 2014Going Deeper with Convolutionscited by 4,241
  7. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  8. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  9. 2019Searching for MobileNetV3cited by 1,994
  10. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
  11. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
  12. 2020COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Imagescited by 60
8 more
  1. 2020Supporting large-scale image recognition with out-of-domain samplescited by 9
  2. 2021DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognitioncited by 9
  3. 2021Mask or Non-Mask? Robust Face Mask Detector via Triplet-Consistency Representation Learningcited by 3
  4. 2019Multi-label Detection and Classification of Red Blood Cells in Microscopic Imagescited by 1
  5. 2020LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learningcited by 1
  6. 2021Computer-aided Interpretable Features for Leaf Image Classificationcited by 1
  7. 2021RikoNet: A Novel Anime Recommendation Enginecited by 1
  8. 2021Rapid Face Mask Detection and Person Identification Model based on Deep Neural Networkscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 1.

4 papers.

  1. 2026MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generationcited by 12
  2. 2025Character-Centric Understanding of Animated Moviescited by 3
  3. 2023RetriBooru: Leakage-Free Retrieval of Conditions from Reference Images for Subject-Driven Generationcited by 2
  4. 2023MixerFlow: MLP-Mixer meets Normalising Flowscited by 0