Deep Learning for Robust and Explainable Models in Computer Vision
Mohammadreza Amirian
Recent breakthroughs in machine and deep learning (ML and DL) research have
provided excellent tools for leveraging enormous amounts of data and optimizing
huge models with millions of parameters to obtain accurate networks for image
processing. These developments open up tremendous opportunities for using
artificial intelligence (AI) in the automation and human assisted AI industry.
However, as more and more models are deployed and used in practice, many
challenges have emerged. This thesis presents various approaches that address
robustness and explainability challenges for using ML and DL in practice.
  Robustness and reliability are the critical components of any model before
certification and deployment in practice. Deep convolutional neural networks
(CNNs) exhibit vulnerability to transformations of their inputs, such as
rotation and scaling, or intentional manipulations as described in the
adversarial attack literature. In addition, building trust in AI-based models
requires a better understanding of current models and developing methods that
are more explainable and interpretable a priori.
  This thesis presents developments in computer vision models' robustness and
explainability. Furthermore, this thesis offers an example of using vision
models' feature response visualization (models' interpretations) to improve
robustness despite interpretability and robustness being seemingly unrelated in
the related research. Besides methodological developments for robust and
explainable vision models, a key message of this thesis is introducing model
interpretation techniques as a tool for understanding vision models and
improving their design and robustness. In addition to the theoretical
developments, this thesis demonstrates several applications of ML and DL in
different contexts, such as medical imaging and affective computing.