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Computer Science > Computer Vision and Pattern Recognition

arXiv:1801.01451v3 (cs)
[Submitted on 15 Dec 2017 (v1) , last revised 17 Apr 2025 (this version, v3)]

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

Title: 降低深度网络复杂度的稀疏分层傅里叶交互网络

Authors:Andrew Kiruluta, Samantha Williams
Abstract: 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.
Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Machine Learning (cs.LG)
Cite as: arXiv:1801.01451 [cs.CV]
  (or arXiv:1801.01451v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.1801.01451

Submission history

From: Andrew Kiruluta [view email]
[v1] Fri, 15 Dec 2017 20:30:09 UTC (565 KB)
[v2] Thu, 7 Jun 2018 12:09:37 UTC (1 KB)
[v3] Thu, 17 Apr 2025 19:06:38 UTC (128 KB)
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