论文标题
可逆的登图
Invertible DenseNets
论文作者
论文摘要
我们引入了可逆密度网络（I-Densenets），这是残差流的更有效替代品。该方法依赖于对登录中串联的Lipschitz连续性的分析，我们通过满足Lipschitz的约束来实现网络的可逆性。此外，我们通过提出可学习的串联来扩展此方法，该串联不仅改善了模型性能，而且还表明了串联表示的重要性。我们证明了i-densenets和剩余流量在玩具，MNIST和CIFAR10数据上的性能。两种I-Densenets在平等参数预算下所有被考虑的数据集上都以负模样评估的剩余流量优于剩余流量。
We introduce Invertible Dense Networks (i-DenseNets), a more parameter efficient alternative to Residual Flows. The method relies on an analysis of the Lipschitz continuity of the concatenation in DenseNets, where we enforce the invertibility of the network by satisfying the Lipschitz constraint. Additionally, we extend this method by proposing a learnable concatenation, which not only improves the model performance but also indicates the importance of the concatenated representation. We demonstrate the performance of i-DenseNets and Residual Flows on toy, MNIST, and CIFAR10 data. Both i-DenseNets outperform Residual Flows evaluated in negative log-likelihood, on all considered datasets under an equal parameter budget.
