no code implementations • 24 Sep 2021 • Mert Asim Karaoglu, Nikolas Brasch, Marijn Stollenga, Wolfgang Wein, Nassir Navab, Federico Tombari, Alexander Ladikos
The results of our experiments show that the proposed method improves the network's performance on real images by a considerable margin and can be employed in 3D reconstruction pipelines.
no code implementations • NeurIPS 2014 • Marijn Stollenga, Jonathan Masci, Faustino Gomez, Juergen Schmidhuber
It harnesses the power of sequential processing to improve classification performance, by allowing the network to iteratively focus its internal attention on some of its convolutional filters.
Ranked #181 on Image Classification on CIFAR-10