  The performance of multi-domain image-to-image translation has been
significantly improved by recent progress in deep generative models. Existing
approaches can use a unified model to achieve translations between all the
visual domains. However, their outcomes are far from satisfying when there are
large domain variations. In this paper, we reveal that improving the sample
selection strategy is an effective solution. To select informative samples, we
dynamically estimate sample importance during the training of Generative
Adversarial Networks, presenting Informative Sample Mining Network. We
theoretically analyze the relationship between the sample importance and the
prediction of the global optimal discriminator. Then a practical importance
estimation function for general conditions is derived. Furthermore, we propose
a novel multi-stage sample training scheme to reduce sample hardness while
preserving sample informativeness. Extensive experiments on a wide range of
specific image-to-image translation tasks are conducted, and the results
demonstrate our superiority over current state-of-the-art methods.
