The field of domain adaptation and robustness is rapidly advancing, with a focus on developing innovative methods to improve model performance in the presence of domain shifts and adversarial attacks. Recent research has emphasized the importance of unsupervised domain adaptation, robustness against adversarial attacks, and out-of-domain robustness in real-world computer vision applications. Notably, new paradigms and algorithms have been proposed to address the entanglement challenge in unsupervised domain adaptation and to improve the robustness of deep neural networks. Additionally, techniques such as frequency decomposition, dataset-agnostic augmentation, and gradient-guided augmentation have shown promise in enhancing model robustness and adaptability. Overall, the field is moving towards developing more robust and adaptable models that can effectively handle domain shifts and adversarial attacks. Noteworthy papers include: Unsupervised Robust Domain Adaptation, which proposes a novel paradigm and algorithm for unsupervised robust domain adaptation, and D-GAP, which introduces a dataset-agnostic and gradient-guided augmentation method to improve out-of-domain robustness.