论文标题
权重共享跑赢大盘的随机体系结构搜索吗？对金枪鱼的调查
Can weight sharing outperform random architecture search? An investigation with TuNAS
论文作者
论文摘要
基于体重共享的有效神经体系结构搜索方法在使神经体系结构搜索计算机视觉模型方面表现出了良好的希望。但是，存在持续的争论，这些有效方法是否比随机搜索要好得多。在这里，我们在一个逐渐更大，更具挑战性的搜索空间的家族上进行了有效和随机搜索方法之间进行详尽的比较，以进行图像分类和图像分类和检测。尽管这两种方法的效率均取决于问题，但我们的实验表明，有效的搜索方法可以通过随机搜索提供大量的收益。此外，我们提出和评估技术，以提高搜索架构的质量并减少对手动超参数调整的需求。 源代码和实验数据可从https://github.com/google-research/google-research/tree/tree/master/tunas获得
Efficient Neural Architecture Search methods based on weight sharing have shown good promise in democratizing Neural Architecture Search for computer vision models. There is, however, an ongoing debate whether these efficient methods are significantly better than random search. Here we perform a thorough comparison between efficient and random search methods on a family of progressively larger and more challenging search spaces for image classification and detection on ImageNet and COCO. While the efficacies of both methods are problem-dependent, our experiments demonstrate that there are large, realistic tasks where efficient search methods can provide substantial gains over random search. In addition, we propose and evaluate techniques which improve the quality of searched architectures and reduce the need for manual hyper-parameter tuning. Source code and experiment data are available at https://github.com/google-research/google-research/tree/master/tunas
