论文标题
辍学可以模拟样品选择技术的指数数量
Dropout can Simulate Exponential Number of Models for Sample Selection Techniques
论文作者
论文摘要
在文献中通常在文献中使用了两个模型，用于基于样本选择的方法，用于嘈杂的标签训练。同时，众所周知，在网络中存在时，辍学会训练子网组合。我们通过训练单个辍学模型来展示如何利用辍学的属性来培训指数数的共享模型。我们展示了如何修改现有的两个基于模型的样本选择方法，以使用指数级的共享模型。使用与辍学的单个模型不仅更方便，而且这种方法还将辍学的自然好处与训练指数型模型的自然好处相结合，从而改善了结果。
Following Coteaching, generally in the literature, two models are used in sample selection based approaches for training with noisy labels. Meanwhile, it is also well known that Dropout when present in a network trains an ensemble of sub-networks. We show how to leverage this property of Dropout to train an exponential number of shared models, by training a single model with Dropout. We show how we can modify existing two model-based sample selection methodologies to use an exponential number of shared models. Not only is it more convenient to use a single model with Dropout, but this approach also combines the natural benefits of Dropout with that of training an exponential number of models, leading to improved results.
