Flatness Aware

Flatness-aware methods in machine learning aim to improve model generalization by finding flatter minima in the loss landscape, a characteristic empirically linked to better out-of-distribution performance. Current research focuses on understanding the relationship between flatness and transferability of adversarial examples, as well as the implicit biases introduced by optimizers like SGD in various learning paradigms (e.g., multitask, continual learning). These investigations, often employing deep linear networks or analyzing stochastic gradient descent dynamics, are crucial for developing more robust and generalizable machine learning models across diverse applications.