Convolutional Neural Networks have been used in a variety of image related applications after their rise in popularity due to ImageNet competition. Convolutional Neural Networks have shown remarkable results in applications including face recognition, moving target detection and tracking, classification of food based on the calorie content and many more. Designing of Convolutional Neural Networks requires experts having a cross domain knowledge and it is laborious, which requires a lot of time for testing different values for different hyperparameter along with the consideration of different configurations of existing architectures. Neural Architecture Search is an automated way of generating Neural Network architectures which saves researchers from all the brute-force testing trouble, but with the drawback of consuming a lot of computational resources for a prolonged period. In this paper, we propose an automated Neural Architecture Search framework DQNAS, guided by the principles of Reinforcement Learning along with One-shot Training which aims to generate neural network architectures that show superior performance and have minimum scalability problem.
翻译:由于图像网络的竞争,在各种与图像有关的应用中,使用进化神经网络在由于图像网络竞争而受欢迎程度上升之后,在各种与图像相关的应用中已经使用了进化神经网络。进化神经网络在应用方面已经显示出显著的成果,包括面部识别、移动目标检测和跟踪、根据卡路里含量对食物进行分类等等。设计进化神经网络需要具有跨领域知识的专家,而且这种设计很费力,这需要大量时间来测试不同超光谱仪的不同值,同时考虑现有结构的不同配置。神经建筑搜索是一种自动生成神经网络结构的方法,它使研究人员免于所有粗力测试的麻烦,但是由于长期消耗大量计算资源的缺陷。在本文中,我们提出了一个自动化神经结构搜索框架QNAS,以强化学习原则为指导,同时进行一发式培训,目的是生成显示高性能和最小可缩度问题的神经网络结构。