# Understanding and mitigating exploding inverses in invertible neural networks

@inproceedings{Behrmann2020UnderstandingAM, title={Understanding and mitigating exploding inverses in invertible neural networks}, author={Jens Behrmann and Paul Vicol and Kuan-Chieh Wang and Roger Baker Grosse and J{\"o}rn-Henrik Jacobsen}, booktitle={International Conference on Artificial Intelligence and Statistics}, year={2020} }

Invertible neural networks (INNs) have been used to design generative models, implement memory-saving gradient computation, and solve inverse problems. In this work, we show that commonly-used INN architectures suffer from exploding inverses and are thus prone to becoming numerically non-invertible. Across a wide range of INN use-cases, we reveal failures including the non-applicability of the change-of-variables formula on in- and out-of-distribution (OOD) data, incorrect gradients for memory…

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