_:n8798c06f26af4e279b9770aef7c41418xb51
<http://schema.org/description>
"Inducing symmetry equivariance in deep neural architectures has resolved into\nimproved data efficiency and generalization. In this work, we utilize the\nconcept of scale and translation equivariance to tackle the problem of learning\non time-series from raw waveforms. As a result, we obtain representations that\nlargely resemble those of the wavelet transform at the first layer, but that\nevolve into much more descriptive ones as a function of depth. Our empirical\nresults support the suitability of our Wavelet Networks which with a simple\narchitecture design perform consistently better than CNNs on raw waveforms and\non par with spectrogram-based methods."
<https://deepai.org/profile/mark-hoogendoorn>