Educating neural networks

Mouath Aouayeb

mar. 31 2022

Room 229 / Zoom

It is more than hundreds of years of evolution of our education system. Thanks to that, today, the growth of the research is astonishing. Now we are making machines learn. And new robust and optimized models are trained day after day: from Neural Network to CNN to ViT. So, if we consider the DL models as the students of the machine education system,one could ask: Is ViT a Ph.D. student? This talk presents an analogy between the human education system and the deep learning system. Furthermore, different techniques dedicated to training transformers on mid-small databases alongside a novel hybrid model of ViT and CNN are presented.

illustration

Xu Yufei, Zhang Qiming, Zhang Jing and Tao Dacheng. 2021. ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias.

Yahui Liu, Enver Sangineto, Wei Bi, Nicu Sebe, Bruno Lepri and Marco De Nadai. 2021. Efficient Training of Visual Transformers with Small Datasets.

Pierre Foret, Ariel Kleiner, Hossein Mobahi and Behnam Neyshabur. 2020. Sharpness-Aware Minimization for Efficiently Improving Generalization.

Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer and Balaji Lakshminarayanan. 2019. AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.