报告时间：12月29日9：50

报告地点：计算机大楼A411

报 告 人：沈春华

报告题目：

Fast Neural Architecture Search of Compact Semantic Segmentation Models

内容简介：

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.

主讲人简介：

Chunhua Shen is a Professor at School of Computer Science, University of Adelaide. He is a Project Leader and Chief Investigator at the Australian Research Council Centre of Excellence for Robotic Vision (ACRV), for which he leads the project on machine learning for robotic vision. Before he moved to Adelaide as a Senior Lecturer, he was with the computer vision program at NICTA (National ICT Australia), Canberra Research Laboratory for about six years. His research interests are in the intersection of computer vision and statistical machine learning. Recent work has been on large-scale image retrieval and classification, object detection and pixel labelling using deep learning.

He studied at Nanjing University, at Australian National University, and received his PhD degree from the University of Adelaide. From 2012 to 2016, he holds an Australian Research Council Future Fellowship. He served as Associate Editor of IEEE Transactions on Neural Networks and Learning Systems.

沈春华博士现任澳大利亚阿德雷德大学计算机科学学院教授(终身教职)。2011之前在澳大利亚国家信息通讯技术研究院堪培拉实验室的计算机视觉组工作近6年。目前主要从事统计机器学习以及计算机视觉领域的研究工作。主持多项科研课题，在重要国际学术期刊和会议发表论文100余篇。2015,2016年担任IEEE Transactions on Neural Networks and Learning Systems 副主编。多次担任重要国际学术会议(ICCV, CVPR, ECCV等)程序委员。曾在南京大学(本科及硕士)，澳大利亚国立大学(硕士)学习，并在阿德雷德大学获得计算机视觉方向的博士学位。2012年被澳大利亚研究理事会(Australian Research Council)授予Future Fellowship。

更多信息见: cs.adelaide.edu.au/~chhshen/