Understanding and mitigating exploding inverses in invertible neural networks

Behrmann Jens
Behrmann Jens
Vicol Paul
Vicol Paul
Wang Kuan-Chieh
Wang Kuan-Chieh
Jacobsen Jörn-Henrik
Jacobsen Jörn-Henrik
Cited by: 0|Views30

Abstract:

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,...More

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