TY - GEN
T1 - Taming Normalizing Flows
AU - Malnick, Shimon
AU - Avidan, Shai
AU - Fried, Ohad
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/1/3
Y1 - 2024/1/3
N2 - We propose an algorithm for taming Normalizing Flow models - changing the probability that the model will produce a specific image or image category. We focus on Normalizing Flows because they can calculate the exact generation probability likelihood for a given image. We demonstrate taming using models that generate human faces, a subdomain with many interesting privacy and bias considerations. Our method can be used in the context of privacy, e.g., removing a specific person from the output of a model, and also in the context of debiasing by forcing a model to output specific image categories according to a given distribution. Taming is achieved with a fast fine-tuning process without retraining from scratch, achieving the goal in a matter of minutes. We evaluate our method qualitatively and quantitatively, showing that the generation quality remains intact, while the desired changes are applied.
AB - We propose an algorithm for taming Normalizing Flow models - changing the probability that the model will produce a specific image or image category. We focus on Normalizing Flows because they can calculate the exact generation probability likelihood for a given image. We demonstrate taming using models that generate human faces, a subdomain with many interesting privacy and bias considerations. Our method can be used in the context of privacy, e.g., removing a specific person from the output of a model, and also in the context of debiasing by forcing a model to output specific image categories according to a given distribution. Taming is achieved with a fast fine-tuning process without retraining from scratch, achieving the goal in a matter of minutes. We evaluate our method qualitatively and quantitatively, showing that the generation quality remains intact, while the desired changes are applied.
KW - Algorithms
KW - Explainable
KW - accountable
KW - ethical computer vision
KW - fair
KW - privacy-preserving
UR - http://www.scopus.com/inward/record.url?scp=85191984837&partnerID=8YFLogxK
U2 - 10.1109/WACV57701.2024.00458
DO - 10.1109/WACV57701.2024.00458
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AN - SCOPUS:85191984837
T3 - Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
SP - 4632
EP - 4642
BT - Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Y2 - 4 January 2024 through 8 January 2024
ER -