论文标题
联合搜索数据增强政策和网络体系结构
Joint Search of Data Augmentation Policies and Network Architectures
论文作者
论文摘要
训练深神经网络的通用管道包括几个构建块，例如数据增强和网络体系结构选择。 Automl是一个研究领域，旨在自动设计这些部分，但是大多数方法都独立探索每个部分，因为同时搜索所有部分更具挑战性。在本文中，我们提出了一种联合优化方法，用于数据增强策略和网络体系结构，以使培训管道的设计更加自动化。我们方法的核心思想是使整个部分可区分。所提出的方法结合了用于增强策略搜索和网络体系结构搜索的可区分方法，以端到端的方式共同优化它们。实验结果表明，我们的方法达到了与独立搜索的结果相比的竞争性或卓越性能。
The common pipeline of training deep neural networks consists of several building blocks such as data augmentation and network architecture selection. AutoML is a research field that aims at automatically designing those parts, but most methods explore each part independently because it is more challenging to simultaneously search all the parts. In this paper, we propose a joint optimization method for data augmentation policies and network architectures to bring more automation to the design of training pipeline. The core idea of our approach is to make the whole part differentiable. The proposed method combines differentiable methods for augmentation policy search and network architecture search to jointly optimize them in the end-to-end manner. The experimental results show our method achieves competitive or superior performance to the independently searched results.
