论文标题
统一的梯度重新加权用于模型偏见，并具有用于源分离的应用
Unified Gradient Reweighting for Model Biasing with Applications to Source Separation
论文作者
论文摘要
最近的深度学习方法表明，音频源分离任务有了很大的改进。但是，绝大多数此类工作的重点是改善平均分离性能，常常忽略检查或控制结果的分布。在本文中，我们提出了一个简单的统一梯度重新加权方案，并具有轻巧的修改，以使模型的学习过程偏差，并将其转向一定的结果分布。更具体地说，我们使用用户指定的概率分布将每批的梯度更新重新持续。我们将此方法应用于各种源分离任务，以将模型的操作点转移到不同的目标。我们证明了我们统一重新加权方案的不同参数化可以用于解决几个现实世界中的问题，例如不可靠的分离估计。我们的框架使用户能够控制最差和平均性能之间的稳健性权衡。此外，我们在实验上表明，我们的统一重新加权方案也可以使用，以将模型的重点转移到用户指定的声音类方面更准确，甚至更轻松地示例以实现更快的融合。
Recent deep learning approaches have shown great improvement in audio source separation tasks. However, the vast majority of such work is focused on improving average separation performance, often neglecting to examine or control the distribution of the results. In this paper, we propose a simple, unified gradient reweighting scheme, with a lightweight modification to bias the learning process of a model and steer it towards a certain distribution of results. More specifically, we reweight the gradient updates of each batch, using a user-specified probability distribution. We apply this method to various source separation tasks, in order to shift the operating point of the models towards different objectives. We demonstrate different parameterizations of our unified reweighting scheme can be used towards addressing several real-world problems, such as unreliable separation estimates. Our framework enables the user to control a robustness trade-off between worst and average performance. Moreover, we experimentally show that our unified reweighting scheme can also be used in order to shift the focus of the model towards being more accurate for user-specified sound classes or even towards easier examples in order to enable faster convergence.
