  Model inversion, whose goal is to recover training data from a pre-trained
model, has been recently proved feasible. However, existing inversion methods
usually suffer from the mode collapse problem, where the synthesized instances
are highly similar to each other and thus show limited effectiveness for
downstream tasks, such as knowledge distillation. In this paper, we propose
Contrastive Model Inversion~(CMI), where the data diversity is explicitly
modeled as an optimizable objective, to alleviate the mode collapse issue. Our
main observation is that, under the constraint of the same amount of data,
higher data diversity usually indicates stronger instance discrimination. To
this end, we introduce in CMI a contrastive learning objective that encourages
the synthesizing instances to be distinguishable from the already synthesized
ones in previous batches. Experiments of pre-trained models on CIFAR-10,
CIFAR-100, and Tiny-ImageNet demonstrate that CMI not only generates more
visually plausible instances than the state of the arts, but also achieves
significantly superior performance when the generated data are used for
knowledge distillation. Code is available at
\url{https://github.com/zju-vipa/DataFree}.
