This is the first part of my interview with Hamid Eghbal-zadeh, post-doc at the Johannes Kepler University at the Institute of Machine Learning.

In the interview, we are talking about his research on a series of different aspects of representation learning with deep neural networks in order to make them more robust and improve their out-of-distribution behavior.

In this first part, we are talking about the origin of representation learning and data augmentation. Hamid explains his research on the effects of representation learning on model training and highlights some of the important caveats that data augmentation can have on the robustness of your models.

References: