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 conclude by contextualising our contribution to diversity within the wider debate on bias, fairness and representation in generative machine learning.

通过平衡训练数据集中的模式，我们提出了一种多样性权重训练方案，以增加模型的输出多样性，并在受控环境下进行了实验，证明了我们的方法的潜力，这将有助于解决生成式机器学习中偏差、公正性和代表性等更广泛辩论中的多样性问题。

通过多样性权重实现生成模型的模式平衡