Deep Adaptive Wavelet Network
Deep Adaptive Wavelet Network
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DOI：
10.1109/wacv45572.2020.9093580
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
M. Bastidas-Rodríguez;Adrien Gruson;Luisa F. Polanía;S. Fujieda;F. Ortíz;Kohei Takayama;T. Hachisuka
中科院分区：
文献类型：
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作者：
M. Bastidas-Rodríguez;Adrien Gruson;Luisa F. Polanía;S. Fujieda;F. Ortíz;Kohei Takayama;T. Hachisuka
尽管卷积神经网络已经成为许多计算机视觉领域的首选方法，但它们仍然缺乏可解释性，并且通常是在繁琐的试错过程中手动设计的。本文旨在通过提出一种深度神经网络来克服这些局限性，该深度神经网络以系统的方式设计并且可解释，通过将多分辨率分析集成到深度神经网络设计的核心。通过使用提升方案，可以生成小波表示并设计能够以端到端形式学习小波系数的网络。与最先进的架构相比，该模型需要更少的超参数调整，并在图像分类任务中实现了具有竞争力的准确性。本研究实施的准则可在https://github.com/mxbastidasr/DAWN_WACV2020上获得
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