2k citations · 6.6k across the 78 of their papers we have counts for
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A Kernel Perspective for Regularizing Deep Neural Networks
Alberto Bietti, Grégoire Mialon, Dexiong Chen +1
We propose a new point of view for regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space (RKHS). Even though this norm cannot be computed, it ad…
Extracting representations of cognition across neuroimaging studies improves brain decoding
Arthur Mensch, Julien Mairal, Bertrand Thirion +1
Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical…
On the Importance of Visual Context for Data Augmentation in Scene Understanding
Nikita Dvornik, Julien Mairal, Cordelia Schmid
Performing data augmentation for learning deep neural networks is known to be important for training visual recognition systems. By artificially increasing the number of training e…
Modeling Visual Context is Key to Augmenting Object Detection Datasets
Nikita Dvornik, Julien Mairal, Cordelia Schmid
Performing data augmentation for learning deep neural networks is well known to be important for training visual recognition systems. By artificially increasing the number of train…
Unsupervised Learning of Artistic Styles with Archetypal Style Analysis
Daan Wynen, Cordelia Schmid, Julien Mairal
In this paper, we introduce an unsupervised learning approach to automatically discover, summarize, and manipulate artistic styles from large collections of paintings. Our method i…