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.

翻译：我们提出一种用于驯服归一化流模型的算法——即改变模型生成特定图像或图像类别的概率。我们聚焦于归一化流，因为这类模型能够计算给定图像的精确生成概率似然。通过生成人脸的模型（一个涉及诸多隐私与偏差考量的子领域）展示了驯服过程。我们的方法可应用于隐私场景（例如从模型输出中删除特定个体），亦可用于去偏场景——通过迫使模型按既定目标分布输出特定图像类别。驯服通过快速微调实现，无需从头训练模型，数分钟内即可达成目标。我们从定性与定量两个维度评估该方法，结果表明在实现所需变更的同时，生成质量得以保持完整。