Papers › MintNet: Building Invertible Neural Networks with Masked Convolutions

MintNet: Building Invertible Neural Networks with Masked Convolutions

18 Jul 2019NeurIPS 2019 12arXiv:1907.07945archive 2025-07-28

Yang Song, Chenlin Meng, Stefano Ermon

We propose a new way of constructing invertible neural networks by combining simple building blocks with a novel set of composition rules. This leads to a rich set of invertible architectures, including those similar to ResNets. Inversion is achieved with a locally convergent iterative procedure that is parallelizable and very fast in practice. Additionally, the determinant of the Jacobian can be computed analytically and efficiently, enabling their generative use as flow models. To demonstrate their flexibility, we show that our invertible neural networks are competitive with ResNets on MNIST and CIFAR-10 classification. When trained as generative models, our invertible networks achieve competitive likelihoods on MNIST, CIFAR-10 and ImageNet 32x32, with bits per dimension of 0.98, 3.32 and 4.06 respectively.

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