Hybrid pooling with wavelets for CNN

What is it about?

The need to detect and classify objects correctly is a constant challenge, being able to recognize them at different scales and scenarios, sometimes cropped or badly lit is not an easy task. Convolutional neural networks (CNN) have become a widely applied technique since they are completely trainable and suitable to extract features. Here, we propose a hybrid pooling method that incorporates multiresolution analysis within the CNN layers to reduce the feature map size without losing details.

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Photo by Clint Adair on Unsplash

Photo by Clint Adair on Unsplash

Why is it important?

We propose a hybrid pooling method that incorporates multiresolution analysis within the CNN layers to reduce the feature map size without losing details. To prevent relevant information from losing during the downsampling process an existing pooling method is combined with the wavelet transform technique, keeping those details "alive" and enriching other stages of the CNN.

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