# Deep Adaptive Wavelet Network Source: [Deep Adaptive Wavelet Network]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:58:21.378Z Authors: Maria Ximena Bastidas Rodriguez, Adrien Gruson, Luisa F. Polania, Shin Fujieda, Flavio Prieto Ortiz, Kohei Takayama, Toshiya Hachisuka Published: 2019-12-10T22:43:16.000Z ## Abstract 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 ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()