AM-LFS：损失函数的AutoML搜索
AM-LFS: AutoML for Loss Function Search
摘要：
设计一个有效的损失函数在可视化分析中起着重要的作用。大多数现有的损失函数设计依赖于手工制作的算法，需要领域专家探索大的设计空间，这通常是次优的和耗时的。在本文中，我们提出了AutoML的损失函数搜索（AM-LFS），它利用REINFORCE搜索损失函数在训练过程中。这项工作的关键贡献是设计的搜索空间，可以保证在不同的视觉任务的推广和可移植性，包括一堆现有的流行损失函数在一个统一的制定。我们还提出了一个有效的优化框架，可以在训练过程中动态优化损失函数的分布参数。在四个基准数据集上的广泛实验结果表明，在没有任何技巧的情况下，我们的方法在各种计算机视觉任务中的性能优于现有的手工制作的损失函数。
英文摘要：
Designing an effective loss function plays an important role in visual analysis. Most existing loss function designs rely on hand-crafted heuristics that require domain experts to explore the large design space, which is usually sub-optimal and time-consuming. In this paper, we propose AutoML for Loss Function Search (AM-LFS) which leverages REINFORCE to search loss functions during the training process. The key contribution of this work is the design of search space which can guarantee the generalization and transferability on different vision tasks by including a bunch of existing prevailing loss functions in a unified formulation. We also propose an efficient optimization framework which can dynamically optimize the parameters of loss function's distribution during training. Extensive experimental results on four benchmark datasets show that, without any tricks, our method outperforms existing hand-crafted loss functions in various computer vision tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
