Enhancing robustness to novel visual defects through StyleGAN latent space navigation: a manufacturing use case
Author:
Funder
Horizon 2020 Framework Programme
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02415-1.pdf
Reference48 articles.
1. Abdal, R., Qin, Y., & Wonka, P. (2019). Image2stylegan: How to embed images into the stylegan latent space? In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, pp. 4431–4440. https://doi.org/10.1109/ICCV.2019.00453
2. Abdal, R., Zhu, P., Mitra, N. J., & Wonka, P. (2021). Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. ACM Transactions on Graphics. https://doi.org/10.1145/3447648
3. Ahuja, N.A., Ndiour, I., Kalyanpur, T., & Tickoo, O. (2019). Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection. https://doi.org/10.48550/ARXIV.1909.11786. arXiv: 1909.11786
4. Akcay, S., Ameln, D., Vaidya, A., Lakshmanan, B., Ahuja, N., & Genc, U. (2022). Anomalib: A Deep Learning Library for Anomaly Detection.
5. Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2019). Ganomaly: Semi-supervised anomaly detection via adversarial training. In C. V. Jawahar, H. Li, G. Mori, & K. Schindler (Eds.), Computer Vision - ACCV 2018 (pp. 622–637). Cham: Springer.
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