Deep Adaptive Wavelet Network

Maria Ximena Bastidas Rodriguez, Adrien Gruson, Luisa Polania, Shin Fujieda, Flavio Prieto, Kohei Takayama, Toshiya Hachisuka; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 3111-3119

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 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. The Code implemented for this research is available at https://github.com/mxbastidasr/DAWN_WACV2020