no code implementations • 1 Dec 2022 • Hubert Leterme, Kévin Polisano, Valérie Perrier, Karteek Alahari
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.
no code implementations • 19 Sep 2022 • Hubert Leterme, Kévin Polisano, Valérie Perrier, Karteek Alahari
This paper focuses on improving the mathematical interpretability of convolutional neural networks (CNNs) in the context of image classification.
no code implementations • 1 Jan 2021 • Hubert Leterme, Kévin Polisano, Valérie Perrier, Karteek Alahari
In this paper, we target an important issue of deep convolutional neural networks (CNNs) — the lack of a mathematical understanding of their properties.