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From CNNs to Shift-Invariant Twin Models Based on Complex Wavelets
Arguably, our approach's emphasis on retaining high-frequency details contributes to a better balance between shift invariance and information preservation, resulting in improved performance.
On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks
This paper focuses on improving the mathematical interpretability of convolutional neural networks (CNNs) in the context of image classification.
Dual-Tree Wavelet Packet CNNs for Image Classification
In this paper, we target an important issue of deep convolutional neural networks (CNNs) — the lack of a mathematical understanding of their properties.
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