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/PRNewswire/ The Edge AI and Vision Alliance today announced the 2023 winners of the Edge AI and Vision Product of the Year Awards. The Awards celebrate the.
Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corporations.
Despite their popularity, deep learning methods still have two main limitations: large memory consumption and catastrophic knowledge forgetting. First, DL algorithms use very deep neural networks (DNNs) with many billion parameters, which have a big model size and a slow inference speed. This restricts the application of DNNs in resource-constraint devices such as mobile phones and autonomous vehicles. Second, DNNs are known to suffer from catastrophic forgetting. When incrementally le
With the possibility of deceiving deep learning models by appropriately modifying images verified, lots of researches on adversarial attacks and adversarial defenses have been carried out in academia. However, there is few research on adversarial attacks and adversarial defenses of point cloud semantic segmentation models, especially in the field of autonomous driving. The stability and robustness of point cloud semantic segmentation models are our primary concerns in this paper. Aiming at the point cloud segmentation model RangeNet++ in the field of autonomous driving, we propose novel approaches to improve the security and anti-attack capability of the RangeNet++ model. One is to calculate the local geometry that can reflect the surface shape of the point cloud based on the range image. The other is to obtain a general adversarial sample related only to the image itself and closer to the real world based on the range image, then add it into the training set for training. The experime