论文标题
多尺度体系结构很重要：基于基于流量的无损压缩的对抗性鲁棒性
Multi-Scale Architectures Matter: On the Adversarial Robustness of Flow-based Lossless Compression
论文作者
论文摘要
作为一种概率建模技术，基于流的模型在无损压缩\ cite {idf，idf ++，lbb，ivpf，iflow}的领域表现出了巨大的潜力。与其他深层生成模型（例如自动回归，VAE）\ cite {bitswap，hilloc，pixelcnn ++，pixelsnail}，这些模型明确地对数据分布概率进行了建模，基于流的模型的表现更好，因为它们的出色概率密度估计和满意度的概率和满意度的介入速度。在基于流量的模型中，多尺度体系结构提供了从浅层到输出层的快捷方式，从而大大降低了计算复杂性并避免添加更多层时性能降解。这对于构建基于先进的基于流动的可学习的射击映射至关重要。此外，实用压缩任务中模型设计的轻量级要求表明，具有多尺度体系结构的流动在编码复杂性和压缩效率之间取得了最佳的权衡。
As a probabilistic modeling technique, the flow-based model has demonstrated remarkable potential in the field of lossless compression \cite{idf,idf++,lbb,ivpf,iflow},. Compared with other deep generative models (eg. Autoregressive, VAEs) \cite{bitswap,hilloc,pixelcnn++,pixelsnail} that explicitly model the data distribution probabilities, flow-based models perform better due to their excellent probability density estimation and satisfactory inference speed. In flow-based models, multi-scale architecture provides a shortcut from the shallow layer to the output layer, which significantly reduces the computational complexity and avoid performance degradation when adding more layers. This is essential for constructing an advanced flow-based learnable bijective mapping. Furthermore, the lightweight requirement of the model design in practical compression tasks suggests that flows with multi-scale architecture achieve the best trade-off between coding complexity and compression efficiency.
