Deep Adaptive Wavelet Network [article]

Maria Ximena Bastidas Rodriguez, Adrien Gruson, Luisa F. Polania, Shin Fujieda, Flavio Prieto Ortiz, Kohei Takayama, Toshiya Hachisuka
2019 arXiv   pre-print
Even though convolutional neural networks have become the method of choice in many fields of computer vision, they still lack interpretability and are usually designed manually in a cumbersome trial-and-error process. This paper aims at overcoming those limitations by proposing a deep neural network, which is designed in a systematic fashion and is interpretable, by integrating multiresolution analysis at the core of the deep neural network design. By using the lifting scheme, it is possible to
more » ... generate a wavelet representation and design a network capable of learning wavelet coefficients in an end-to-end form. Compared to state-of-the-art architectures, the proposed model requires less hyper-parameter tuning and achieves competitive accuracy in image classification tasks
arXiv:1912.05035v1 fatcat:mhrhzts3bzct3kfatwrtxmbaxu