At the heart of modern self-driving cars is a complex artificial intelligence system consisting of deep neural networks that perform billions of computations per second to process input images and other sensory data to help the vehicle understand the surrounding environment and make the right control decision. Many state-of-the-art deep neural networks can easily outperform human in many recognition tasks. However, their high performance usually comes at a cost: prohibitive computational complexity. This will translate to expensive hardware and high energy consumption which is not environmentally sustainable. Current research is focusing on developing new architectures that achieve similar level of performance whilst having much less computational cost and model complexity. In this seminar, Dr Pham will explain several important deep learning technologies that underpin successful designs to address the challenge and illustrate how they can be used to develop advanced “green” semantic segmentation models. It is expected that the audience will be able to apply key ideas in this talk to solve their problems in computer vision and related domain in a more environmentally sustainable way.
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