Taming Normalizing Flows

Shimon Malnick; Shai Avidan; Ohad Fried

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arXiv · 2211.16488

Taming Normalizing Flows

Shimon Malnick·Shai Avidan·Ohad Fried

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Abstract

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 target distribution. Taming is achieved with a fast fine-tuning process without retraining the model 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.

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Shimon Malnick, Shai Avidan, Ohad Fried. 2022-11-29. Taming Normalizing Flows. https://arxiv.org/abs/2211.16488

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