学术报告

题目：Evolutionary Deep Learning and Applications to Image Classification

           演化深度学习及其在图像分类中的应用

时间：2018年10月17日 上午10:00-11:00

地点：信息科学与工程学院 442 教室

报告摘要：Image classification problems occur in our everyday life. Recognising faces in digital images and diagnosing medical conditions from X-Ray images are just two examples of the many important tasks for which we need computer based image classification systems. Since the 1980s, many image analysis algorithms have been developed. Among those algorithms, deep learning particularly deep convolutional neural networks have received very good success and attracted attentions to industry people and researchers in computer vision and image processing, neural networks, and machine learning. However, there are at least three major limitations in deep convolutional neural networks:  (1) the learning architecture including the number of layers, the number of feature maps in each layer and the number of nodes in each feature map are still very much determined manually via "trial and error", which requires a large amount of hand-crafting/trial time and good domain knowledge. However, such experts are hard to find in many cases, or using such expertise is too expensive.  (2) Almost all the current deep learning algorithms need a large number of examples/instances (e.g. AlphaGo used over 30 million instances) that many problems do not have. (3) Those algorithms require a huge computational cost that big companies such as Google, Baidu, and Microsoft can cope well but most universities and research institutions cannot.

To address these limitations, evolutionary computation techniques start playing a significant role for automatically determining deep structures, transfer functions and parameters to tackle image classification tasks, and have great potential to advance the developments of deep structures and algorithms. This talk will provide an extended view of deep learning, overview the state-of-the-art work in evolutionary deep learning using GAs/PSO/DE, and discuss some recent developments using Genetic Programming (GP) to automatically evolving deep structures and feature construction for image recognition with a highlight of the interpretation capability and visualisation of constructed features. Finally, recent work and ideas on evolutionary deep transfer learning will be discussed.

报告人简介：Professor Mengjie Zhang (张孟杰)

张孟杰教授是新西兰皇家科学院院士，现任新西兰惠灵顿维多利亚大学教授、博士研究生导师，工程学院副院长。张孟杰教授的主要研究领域包括人工智能，深度学习＋自动编程，进化计算，机器学习，数据挖掘，生物信息等。近年来，主持和参与了新西兰Marsden基金、中国国家自然基金等科研项目20余项，发表SCI索引论文100余篇；在遗传规划、学习分类系统、进化特征筛选与大数据降维、进化调度与优化、进化机器视觉与图像分析等5个战略方向方面的研究，处于国际进化计算领域领先位置，享有良好国际声誉。张孟杰教授担任IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Evolutionary Computation Journal (MIT Press), IEEE Transactions Emergent Topics in CI, Genetic Programming and Evolvable Machines (Springer), Applied Soft Computing, and Engineering Applications of Artificial Intelligence等期刊的副主编或编委，兼任IEEE计算智能协会(CIS)进化计算技术委员会（ECTC）主席，IEEE新西兰计算机协会（NZCS）计算机智能专委会主席，IEEE 新西兰中央委员会成员，IEEE、IEEE CS、IEEE CIS、IEEE MSCS高级会员，ACM，ACM SIGEVO会员。

Mengjie Zhang is currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group with over 12 staff members, seven postdocs and over 25 PhD students. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, a member of the Faculty of Graduate Research Board at the University, Associate Dean (Research and Innovation) for Faculty of Engineering, and Chair of the Research Committee for the School of Engineering and Computer Science. His research is mainly focused on evolutionary computation, particularly genetic programming, particle swarm optimisation and learning classifier systems with application areas of computer vision and image processing, multi-objective optimisation, and feature selection and dimension reduction for classification with high dimensions, transfer learning, classification with missing data, and scheduling and combinatorial optimisation. Prof Zhang has published over 500 research papers in fully refereed international journals and conferences in these areas. He has been supervising over 100 research thesis and project students including over 30 PhD students.

He has been serving as an associated editor or editorial board member for ten international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Evolutionary Computation Journal (MIT Press), IEEE Transactions Emergent Topics in CI, Genetic Programming and Evolvable Machines (Springer), Applied Soft Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been a major chair for over ten international conferences including IEEE CEC, GECCO, EvoStar and SEAL. He has also been serving as a steering committee member and a program committee member for over 80 international conferences including all major conferences in evolutionary computation. Since 2007, he has been listed as one of the top five world genetic programming researchers by the GP bibliography. He will chair and host IEEE CEC 2019 Wellington, the Capital City of New Zealand.

Prof Zhang is a Fellow of Royal Society (Academy of Sciences) of New Zealand. He is currently chairing the IEEE CIS Intelligent Systems and Applications Technical Committee. He is the immediate Past Chair for the Emergent Technologies Technical Committee and the IEEE CIS Evolutionary Computation Technical Committee, and a member of the IEEE CIS Award Committee. He is also a vice-chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, a vice-chair of the Task Force on Evolutionary Computer Vision and Image Processing, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.