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Wavelet implicit neural representation (wire) Since the wavelet transform is both space and frequency localized and excels at approximating functions with abrupt changes (tripura & chakraborty, 2022), generation in the wavelet space endowed by the. We propose a new nonlinearity for implicit neural representations (inrs) based on the continuous complex gabor wavelet that has high representation capacity for visual signals
The top row shows two commonly used nonlinearities (1) generation in the wavelet domain Here, we propose adaptive wavelet distillation (awd), a method which aims to distill information from a trained neural network into a wavelet transform
Some researches have begun to try to integrate the good properties of wavelet into anns.
In this work, we introduce wavelet diffusion neural operator (wdno) to address the above two challenges Our wdno method consists of two key innovations
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