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A General Descent Aggregation Framework for Gradient-based Bi-level Optimization. (arXiv:2102.07976v3 [cs.LG] UPDATED)
Jan. 4, 2022, 9:10 p.m. | Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang
cs.CV updates on arXiv.org arxiv.org
In recent years, a variety of gradient-based methods have been developed to
solve Bi-Level Optimization (BLO) problems in machine learning and computer
vision areas. However, the theoretical correctness and practical effectiveness
of these existing approaches always rely on some restrictive conditions (e.g.,
Lower-Level Singleton, LLS), which could hardly be satisfied in real-world
applications. Moreover, previous literature only proves theoretical results
based on their specific iteration strategies, thus lack a general recipe to
uniformly analyze the convergence behaviors of different gradient-based …
More from arxiv.org / cs.CV updates on arXiv.org
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