How to improve generative modeling by better exploiting spatial regularities and coherence in images? We introduce a novel neural network for building image generators (decoders) and apply it to variational autoencoders (VAEs). In our spatial dependency networks (SDNs), feature maps at each level of a deep neural net are computed in a spatially coherent way, using a sequential gating-based mechanism that distributes contextual information across 2-D space. We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation over baseline convolutional architectures and the state-of-the-art among the models within the same class. Furthermore, we demonstrate that SDN can be applied to large images by synthesizing samples of high quality and coherence. In a vanilla VAE setting, we find that a powerful SDN decoder also improves learning disentangled representations, indicating that neural architectures play an important role in this task. Our results suggest favoring spatial dependency over convolutional layers in various VAE settings. The accompanying source code is given at https://github.com/djordjemila/sdn.
翻译:如何通过更好地利用空间规律和图像的一致性来改进基因模型的改进?我们引入了用于建立图像生成器(decoders)的新神经网络,并将其应用于变异自动电解码器(VAEs)。在我们的空间依赖网络(SDNs)中,以空间一致的方式计算深神经网各级的地貌图。在Vanilla VAE环境中,我们发现一个强大的SDN解码器也能改善学习不相干的表达方式,表明神经结构在这项任务中起着重要作用。我们的结果表明,在VAE环境中,基线共变形结构的密度估计和模型中的状态都大大改进了。此外,我们证明SDN可以通过对高质量和一致性样本的合成来应用大型图像。在VAE环境中,我们发现一个强大的SDN解码还可以改进对相交错的表达方式的学习,表明神经结构在这项任务中起着重要作用。我们的结果表明,在VAEE环境中的相变形层中支持空间依赖性。随源代码在 https://girub/drd.