澳大新語 • 2021 UMAGAZINE 24 11 封面專題 • COVER STORY the RIFLE algorithm at the prestigious International Conference on Machine Learning. Faster Training of Learning Models We can remove unnecessary parts of a CNN before retraining it for a new but similar task. Known as ‘pruning', the process can reduce the size of the original network and thus shorten the time required for training the new one. However, pruning sometimes sharply weakens the network’s decision‑making power. To address this problem, researchers at UM, SIAT, and Baidu proposed a new pruning method called ‘Attentive Feature Distillation and Selection’ (AFDS). They used ADFS and some mainstream counterparts to prune a 101‑layer CNN, and then used the final products to identify objects from images stored in six databases. The results show that the AFDS‑pruned network remains very effective despite a 30 per cent cut on the amount of computation. Even when the amount of computation was reduced by 90 per cent, the network accuracy rate stood at 70 per cent, much higher than its competitors. The researchers have presented an academic paper about the AFDS method at the influential International Conference on Learning Representations. Self‑driving cars must be able to identify everything around them. CNNs can usually perform such tasks effectively, but they can be vulnerable when facing cyberattacks or unclear objects like road signs with graffiti. This can cause potentially fatal mistakes like misreading a stop sign as a give‑way sign. Therefore, Prof Xu’s team has developed the ‘LAFEAT’ algorithm, which can make CNNs more robust to adversarial attacks or noise. The new algorithm has outperformed a dozen existing options in computer trials. In mid‑2021, researchers at UM and SIAT presented the LAFEAT algorithm at the Conference on Computer Vision and Pattern Recognition, which has an acceptance rate of just 4.59 per cent. They also used the new algorithm to compete with 1680 teams from around the world at the CVPR Security AI Challenger, an algorithm competition jointly organised by the University of Illinois, Tsinghua University, and Alibaba Security. The team won a second prize, becoming the only winning team from Macao. Stepping into a Driverless Future Prof Xu’s team is also studying technologies to enable better interactions between humans and self‑driving cars. For instance, they are designing natural language processing solutions that will allow cars to respond appropriately to spoken commands. Moreover, they are searching for ways to help cars adapt to the complicated road conditions in Macao, a city known for its narrow roads and abundance of motorbikes. So, when can we take our hands off the wheels? Prof Xu believes that many technological breakthroughs need to be achieved before self‑driving cars can finally go mainstream. A driverless future would also need major transformations in transport and telecommunications infrastructure as well as new legal rules ‑ all requiring the concerted effort of various sectors of the community. Prof Xu says, ‘We will of course continue to advance self‑driving technology, but by launching our bus on the UM campus, we also hope to raise the public’s understanding of self‑driving cars, so that we can be better prepared for their deployment in Macao.’ 卷積神經網絡受攻擊時或會誤判路標 Convolutional neural networks under cyberattacks may misjudge road signs 掃二維碼 觀看訪談片段 Scan the QR code to watch the interview
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