  Accelerating deep neural networks (DNNs) has been attracting increasing
attention as it can benefit a wide range of applications, e.g., enabling mobile
systems with limited computing resources to own powerful visual recognition
ability. A practical strategy to this goal usually relies on a two-stage
process: operating on the trained DNNs (e.g., approximating the convolutional
filters with tensor decomposition) and fine-tuning the amended network, leading
to difficulty in balancing the trade-off between acceleration and maintaining
recognition performance. In this work, aiming at a general and comprehensive
way for neural network acceleration, we develop a Wavelet-like Auto-Encoder
(WAE) that decomposes the original input image into two low-resolution channels
(sub-images) and incorporate the WAE into the classification neural networks
for joint training. The two decomposed channels, in particular, are encoded to
carry the low-frequency information (e.g., image profiles) and high-frequency
(e.g., image details or noises), respectively, and enable reconstructing the
original input image through the decoding process. Then, we feed the
low-frequency channel into a standard classification network such as VGG or
ResNet and employ a very lightweight network to fuse with the high-frequency
channel to obtain the classification result. Compared to existing DNN
acceleration solutions, our framework has the following advantages: i) it is
tolerant to any existing convolutional neural networks for classification
without amending their structures; ii) the WAE provides an interpretable way to
preserve the main components of the input image for classification.
