基于熵感知的加权通道归一化流用于深度生成模型
Entropy-Informed Weighting Channel Normalizing Flow for Deep Generative Models
AI总结：
本文提出了一种基于熵感知的加权通道归一化流，通过引入正则化的Shuffle操作提升深度生成模型的密度估计性能。
摘要：
归一化流（NFs）因其精确的似然估计和高效的采样而在深度生成模型中被广泛应用。然而，它们需要大量的内存，因为潜在空间与输入维度相匹配。多尺度架构通过逐步减少潜在维度同时保持可逆性来解决这一问题。现有的多尺度架构使用简单的静态通道分割，限制了表达能力。为改进这一点，我们引入了一种正则化的、基于特征的Shuffle操作，并将其整合到普通的多尺度架构中。该操作能够自适应地生成通道权重并重新排列潜在变量，在分割前进行洗牌。我们发现这种操作引导变量向熵增大的方向演化，因此我们将带有Shuffle操作的NFs称为熵感知加权通道归一化流（EIW-Flow）。在CIFAR-10、CelebA、ImageNet和LSUN上的广泛实验表明，EIW-Flow在深度生成建模中实现了最先进的密度估计和具有竞争力的样本质量，且计算开销极小。
英文摘要：
Normalizing Flows (NFs) are widely used in deep generative models for their exact likelihood estimation and efficient sampling. However, they require substantial memory since the latent space matches the input dimension. Multi-scale architectures address this by progressively reducing latent dimensions while preserving reversibility. Existing multi-scale architectures use simple, static channel-wise splitting, limiting expressiveness. To improve this, we introduce a regularized, feature-dependent $\mathtt{Shuffle}$ operation and integrate it into vanilla multi-scale architecture. This operation adaptively generates channel-wise weights and shuffles latent variables before splitting them. We observe that such operation guides the variables to evolve in the direction of entropy increase, hence we refer to NFs with the $\mathtt{Shuffle}$ operation as \emph{Entropy-Informed Weighting Channel Normalizing Flow} (EIW-Flow). Extensive experiments on CIFAR-10, CelebA, ImageNet, and LSUN demonstrate that EIW-Flow achieves state-of-the-art density estimation and competitive sample quality for deep generative modeling, with minimal computational overhead.
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