标题： 降低深度网络复杂度的稀疏分层傅里叶交互网络 显示英文标题

标题： Reducing Deep Network Complexity via Sparse Hierarchical Fourier Interaction Networks

摘要： 本文提出了一种稀疏分层傅里叶交互网络，这是一种统一了频率域建模三个互补原则的架构构建块：一种分层的逐块傅里叶变换，能够同时访问局部细节和全局上下文；一种可学习的、可微的保留前K个系数的掩码机制，从而保留最具有信息量的频谱系数，进而利用视觉和语言信号的自然压缩性。 显示英文摘要

摘要： This paper presents a Sparse Hierarchical Fourier Interaction Networks, an architectural building block that unifies three complementary principles of frequency domain modeling: A hierarchical patch wise Fourier transform that affords simultaneous access to local detail and global context; A learnable, differentiable top K masking mechanism which retains only the most informative spectral coefficients, thereby exploiting the natural compressibility of visual and linguistic signals.