Invertible Convolution with Symmetric Paddings
arXiv:2303.17361v1h-index: 58Has Code
Originality Synthesis-oriented
AI Analysis
This work addresses a specific technical challenge in signal processing or deep learning, but it appears incremental as it builds on existing convolution and inversion methods.
The paper tackles the problem of inverting symmetrically padded convolutions by demonstrating that they can be analytically inverted using the Discrete Fourier Transform (DFT), with multiple cases identified where inversion is possible.
We show that symmetrically padded convolution can be analytically inverted via DFT. We comprehensively analyze several different symmetric and anti-symmetric padding modes and show that multiple cases exist where the inversion can be achieved. The implementation is available at \url{https://github.com/prclibo/iconv_dft}.