3 papers
cs.LG2025
Distribution-Aware Tensor Decomposition for Compression of Convolutional Neural Networks
Alper Kalle, Theo Rudkiewicz, Mohamed-Oumar Ouerfelli +1
Neural networks are widely used for image-related tasks but typically demand considerable computing power. Once a network has been trained, however, its memory- and compute-footpri…
cs.CV2024
Universal Scale Laws for Colors and Patterns in Imagery
Rémi Michel, Mohamed Tamaazousti
Distribution of colors and patterns in images is observed through cascades that adjust spatial resolution and dynamics. Cascades of colors reveal the emergent universal property th…
eess.IV2024
Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution
Zakariya Chaouai, Mohamed Tamaazousti
Most of the recent literature on image Super-Resolution (SR) can be classified into two main approaches. The first one involves learning a corruption model tailored to a specific d…