论文标题
驯服标准化流
Taming Normalizing Flows
论文作者
论文摘要
我们提出了一种用于驯服标准化流模型的算法 - 改变模型产生特定图像或图像类别的概率。我们专注于标准化流，因为它们可以计算给定图像的确切生成概率可能性。我们使用产生人脸的模型来证明驯服，这是一个具有许多有趣的隐私和偏见考虑的子域。我们的方法可以用于隐私的上下文，例如，从模型的输出中删除特定的人，也可以通过强迫模型根据给定的目标分布来输出特定图像类别来进行辩护。驯服是通过快速的微调过程实现的，而无需从头开始重述模型，从而在几分钟内实现了目标。我们在定性和定量上评估我们的方法，表明发电质量保持完整，同时应用所需的更改。
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.
