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Explore out-of-distribution generalization challenges in this MIT deep learning lecture that examines how models perform when encountering data different from their training distribution. Delve into adversarial robustness and learn strategies for handling distribution shifts that commonly occur in real-world machine learning applications. Understand the fundamental problems that arise when deploying models in environments where the test data differs significantly from training data, and discover approaches to improve model reliability across varying conditions. Gain insights into the theoretical foundations of OOD generalization while examining practical techniques for building more robust deep learning systems that can maintain performance despite encountering unexpected input distributions.
