WaveNets: Wavelet Channel Attention Networks
This work addresses a specific bottleneck in computer vision for researchers and practitioners by improving channel attention mechanisms, though it is incremental as it builds on existing methods like SENet.
The paper tackles the information loss in channel attention mechanisms caused by Global Average Pooling by proposing WaveNets, which use wavelet transform compression to enhance feature preservation, achieving state-of-the-art results on ImageNet classification.
Channel Attention reigns supreme as an effective technique in the field of computer vision. However, the proposed channel attention by SENet suffers from information loss in feature learning caused by the use of Global Average Pooling (GAP) to represent channels as scalars. Thus, designing effective channel attention mechanisms requires finding a solution to enhance features preservation in modeling channel inter-dependencies. In this work, we utilize Wavelet transform compression as a solution to the channel representation problem. We first test wavelet transform as an Auto-Encoder model equipped with conventional channel attention module. Next, we test wavelet transform as a standalone channel compression method. We prove that global average pooling is equivalent to the recursive approximate Haar wavelet transform. With this proof, we generalize channel attention using Wavelet compression and name it WaveNet. Implementation of our method can be embedded within existing channel attention methods with a couple of lines of code. We test our proposed method using ImageNet dataset for image classification task. Our method outperforms the baseline SENet, and achieves the state-of-the-art results. Our code implementation is publicly available at https://github.com/hady1011/WaveNet-C.