What does "Inner Optimization" mean?

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Inner optimization is a process used to improve the performance of a model in a flexible way. It allows the model to adjust itself based on different situations or data it encounters.

In the context of machine learning, especially when models face challenging tasks or attacks, inner optimization helps to enhance accuracy. Instead of sticking to fixed answers or methods, it allows the model to adapt and find better solutions as it learns.

This method can be particularly useful when a model needs to deal with both regular data (clean accuracy) and challenging or misleading data (robust accuracy). By being able to adjust and search for the best responses dynamically, inner optimization aims to make models stronger and more reliable in different scenarios.

In summary, inner optimization helps models become smarter by enabling them to learn and adapt continuously, leading to better overall performance.