Explore the challenges and solutions for making machine learning models robust to changing visual environments in this conference talk from ROB 2018. Delve into classic domain adaptation techniques, deep domain adaptation methods, and adversarial approaches. Learn about discrepancies between source and target domains, domain adversarial optimization, and the application of GANs for adaptation. Examine real-world examples of adaptation in digit recognition, semantic segmentation, cross-city scenarios, and cross-season environments. Investigate continuous learning strategies, including unsupervised adaptation and replay mechanisms. Analyze experiments with MNIST rotations to understand the balance between adaptation and memory retention. Gain insights into batch and continuous adaptation techniques for improving model performance across diverse visual contexts.