As the application of generative adversarial networks (GANs) expands, it becomes increasingly critical to develop a unified approach that improves performance across various generative tasks. One effective strategy that applies to any machine learning task is identifying harmful instances, whose removal improves the performance. While previous studies have successfully estimated these harmful training instances in supervised settings, their approaches are not easily applicable to GANs. The challenge lies in two requirements of the previous approaches that do not apply to GANs. First, previous approaches require that the absence of a training instance directly affects the parameters. However, in the training for GANs, the instances do not directly affect the generator's parameters since they are only fed into the discriminator. Second, previous approaches assume that the change in loss directly quantifies the harmfulness of the instance to a model's performance, while common types of GAN losses do not always reflect the generative performance. To overcome the first challenge, we propose influence estimation methods that use the Jacobian of the generator's gradient with respect to the discriminator's parameters (and vice versa). Such a Jacobian represents the indirect effect between two models: how removing an instance from the discriminator's training changes the generator's parameters. Second, we propose an instance evaluation scheme that measures the harmfulness of each training instance based on how a GAN evaluation metric (e.g., Inception score) is expected to change by the instance's removal. Furthermore, we demonstrate that removing the identified harmful instances significantly improves the generative performance on various GAN evaluation metrics.

翻译：随着生成对抗网络（GANs）应用范围的扩大，开发一种能够提升各类生成任务性能的统一方法变得日益重要。适用于任何机器学习任务的一种有效策略是识别有害实例，移除这些实例能够提升模型性能。尽管先前研究已在监督学习场景中成功估计了这些有害训练实例，但其方法难以直接应用于GANs。挑战主要源于先前方法的两项要求不适用于GANs：首先，先前方法要求训练实例的缺失能直接影响模型参数，然而在GANs训练中，实例仅输入判别器，不会直接影响生成器的参数；其次，先前方法假设损失函数的变化能直接量化实例对模型性能的有害程度，而常见类型的GAN损失函数并不总能反映生成性能。为克服第一个挑战，我们提出了基于雅可比矩阵的影响估计算法，该算法利用生成器梯度相对于判别器参数的雅可比矩阵（反之亦然）。此类雅可比矩阵表征了两个模型间的间接影响：从判别器训练中移除实例如何改变生成器的参数。其次，我们提出了一种实例评估方案，该方案通过GAN评估指标（例如初始分数）在实例移除后的预期变化来衡量每个训练实例的有害程度。实验进一步证明，移除识别出的有害实例能显著提升多种GAN评估指标下的生成性能。