Towards Mode Balancing of Generative Models via Diversity Weights

Understand

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.