用序列惩罚法约束样本级学习，提升图像处理可靠性。
Sample-wise Constrained Learning via a Sequential Penalty Approach with Applications in Image Processing
- 采用序列惩罚法处理样本级严格约束，避免随意加权。
- 理论证明在深度学习常见假设下算法收敛。
- 图像处理实验验证方法实用且效果稳定。
在许多学习任务中，对单个数据样本的处理应以严格约束形式纳入优化问题，而非依赖任意惩罚项。本文提出一种基于序列惩罚的算法，可有效处理此类约束。该方法在深度学习合理假设下具备收敛性保证。图像处理任务的实验结果表明，该方法在实际应用中具有可行性与有效性。
原文摘要 · Abstract (English)
In many learning tasks, certain requirements on the processing of individual data samples should arguably be formalized as strict constraints in the underlying optimization problem, rather than by means of arbitrary penalties. We show that, in these scenarios, learning can be carried out exploiting a sequential penalty method that allows to properly deal with constraints. The proposed algorithm is shown to possess convergence guarantees under assumptions that are reasonable in deep learning scenarios. Moreover, the results of experiments on image processing tasks show that the method is indeed viable to be used in practice.
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