Automated design of neural network architectures tailored for a specific task is an extremely promising, albeit inherently difficult, avenue to explore. While most results in this domain have been achieved on image classification and language modelling problems, here we concentrate on dense per-pixel tasks, in particular, semantic image segmentation using fully convolutional networks. In contrast to the aforementioned areas, the design choices of a fully convolutional network require several changes, ranging from the sort of operations that need to be used - e.g., dilated convolutions - to a solving of a more difficult optimisation problem. In this work, we are particularly interested in searching for high-performance compact segmentation architectures, able to run in real-time using limited resources. To achieve that, we intentionally over-parameterise the architecture during the training time via a set of auxiliary cells that provide an intermediate supervisory signal and can be omitted during the evaluation phase. The design of the auxiliary cell is emitted by a controller, a neural network with the fixed structure trained using reinforcement learning. More crucially, we demonstrate how to efficiently search for these architectures within limited time and computational budgets. In particular, we rely on a progressive strategy that terminates non-promising architectures from being further trained, and on Polyak averaging coupled with knowledge distillation to speed-up the convergence. Quantitatively, in 8 GPU-days our approach discovers a set of architectures performing on-par with state-of-the-art among compact models on the semantic segmentation, pose estimation and depth prediction tasks.
沈春華博士現任澳大利亞阿德萊德大學計算機科學學院教授。 2011之前在澳大利亞國家信息通訊技術研究院堪培拉實驗室的計算機視覺組工作近6年。 目前主要從事統計機器學習以及計算機視覺領域的研究工作。 主持多項科研課題，在重要國際學術期刊和會議發表論文220餘篇。 擔任或擔任過副主編的期刊包括：Pattern Recognition, IEEE Transactions on Neural Networks and Learning Systems。多次擔任重要國際學術會議(ICCV, CVPR, ECCV等)程序委員。 他曾在南京大學(本科及碩士)，澳大利亞國立大學(碩士)學習，并在阿德萊德大學獲得計算機視覺方向的博士學位。 沈春華教授曾獲得AR C Future Fellowship等榮譽。
