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.

翻译：机器学习和深度学习的最新突破提供了强大的工具，能够利用海量数据并优化包含数百万参数的大型模型，从而获得用于图像处理的精确网络。这些发展为人机协作的人工智能自动化领域带来了巨大机遇。然而，随着越来越多的模型在实践中的部署与应用，诸多挑战也相继涌现。本论文提出了多种方法，以应对在实践应用机器学习和深度学习时面临的鲁棒性与可解释性挑战。鲁棒性和可靠性是任何模型在认证并投入实际应用前的关键组成部分。深度卷积神经网络在输入变换（如旋转和缩放）或对抗攻击文献中描述的有意操纵下表现出脆弱性。此外，要建立对基于人工智能的模型的信任，需要更好地理解现有模型，并开发更具先验可解释性与可理解性的方法。本论文介绍了计算机视觉模型鲁棒性与可解释性方面的发展。尽管在相关研究中，可解释性与鲁棒性看似无关，但本论文通过利用视觉模型的特征响应可视化（模型解释）来提升鲁棒性，提供了具体实例。除了针对鲁棒且可解释的视觉模型的方法论发展，本论文的核心观点是将模型解释技术作为理解视觉模型并改进其设计与鲁棒性的工具。在理论发展之外，本论文还展示了机器学习和深度学习在医学影像、情感计算等不同领域的多项应用。