Large data-driven image models are extensively used to support creative and artistic work. Under the currently predominant distribution-fitting paradigm, a dataset is treated as ground truth to be approximated as closely as possible. Yet, many creative applications demand a diverse range of output, and creators often strive to actively diverge from a given data distribution. We argue that an adjustment of modelling objectives, from pure mode coverage towards mode balancing, is necessary to accommodate the goal of higher output diversity. We present diversity weights, a training scheme that increases a model's output diversity by balancing the modes in the training dataset. First experiments in a controlled setting demonstrate the potential of our method. We discuss connections of our approach to diversity, equity, and inclusion in generative machine learning more generally, and computational creativity specifically. An implementation of our algorithm is available at https://github.com/sebastianberns/diversity-weights

翻译：大规模数据驱动的图像模型被广泛用于支持创意和艺术工作。在当前主流的分布拟合范式下，数据集被视为需要尽可能精确逼近的真实标准。然而，许多创意应用要求多样化的输出，创作者往往力求主动偏离给定的数据分布。我们认为，有必要调整建模目标，从纯粹的模式覆盖转向模式平衡，以适应对更高输出多样性的需求。我们提出了多样性权重，这是一种通过平衡训练数据集中的模式来增加模型输出多样性的训练方案。在受控环境下的初步实验展示了我们方法的潜力。我们探讨了该方法与生成式机器学习中更广泛的多样性、公平性和包容性，特别是计算创造力领域的关联。我们的算法实现可在 https://github.com/sebastianberns/diversity-weights 获取。