Automated/Evolutionary Deep Learning and Applications to Image Classification
[expand title=”Abstract”] 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 Genetic Algorithms (GAs), Particle Swarm Optimisation (PSO) and Differential Evolution (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. If time allows, the talk will discuss GP applications to biomarker detection and peptide detection. [/expand]
Prof. Dr. Mengjie Zhang
Victory University of Wellington
CIS Distinguished Lecturer
Sponsored by the Computational Intelligence Society under its Distinguished Lecturers Program.
Time: Sep. 29th, 2021 – 7 pm (Rio de Janeiro time).
Location: Online event via Zoom.
