The generalization with respect to domain shifts, as they frequently appear in applications such as autonomous driving, is one of the remaining big challenges for deep learning models. Therefore, we propose an exemplar-based style synthesis pipeline to improve domain generalization in semantic segmentation. Our method is based on a novel masked noise encoder for StyleGAN2 inversion. The model learns to faithfully reconstruct the image, preserving its semantic layout through noise prediction. Using the proposed masked noise encoder to randomize style and content combinations in the training set, i.e., intra-source style augmentation (ISSA) effectively increases the diversity of training data and reduces spurious correlation. As a result, we achieve up to $12.4\%$ mIoU improvements on driving-scene semantic segmentation under different types of data shifts, i.e., changing geographic locations, adverse weather conditions, and day to night. ISSA is model-agnostic and straightforwardly applicable with CNNs and Transformers. It is also complementary to other domain generalization techniques, e.g., it improves the recent state-of-the-art solution RobustNet by $3\%$ mIoU in Cityscapes to Dark Z\"urich. In addition, we demonstrate the strong plug-n-play ability of the proposed style synthesis pipeline, which is readily usable for extra-source exemplars e.g., web-crawled images, without any retraining or fine-tuning. Moreover, we study a new use case to indicate neural network's generalization capability by building a stylized proxy validation set. This application has significant practical sense for selecting models to be deployed in the open-world environment. Our code is available at \url{https://github.com/boschresearch/ISSA}.

The generalization with respect to domain shifts, as they frequently appear in applications such as autonomous driving, is one of the remaining big challenges for deep learning models. Therefore, we propose an intra-source style augmentation (ISSA) method to improve domain generalization in semantic segmentation. Our method is based on a novel masked noise encoder for StyleGAN2 inversion. The model learns to faithfully reconstruct the image preserving its semantic layout through noise prediction. Random masking of the estimated noise enables the style mixing capability of our model, i.e. it allows to alter the global appearance without affecting the semantic layout of an image. Using the proposed masked noise encoder to randomize style and content combinations in the training set, ISSA effectively increases the diversity of training data and reduces spurious correlation. As a result, we achieve up to $12.4\%$ mIoU improvements on driving-scene semantic segmentation under different types of data shifts, i.e., changing geographic locations, adverse weather conditions, and day to night. ISSA is model-agnostic and straightforwardly applicable with CNNs and Transformers. It is also complementary to other domain generalization techniques, e.g., it improves the recent state-of-the-art solution RobustNet by $3\%$ mIoU in Cityscapes to Dark Z\"urich.

Rep. Darrell Issa, R-Calif., discusses artificial intelligence and what Congress can do to promote innovation and stay competitive with China. Rep. Darrell Issa, R-Calif., on Tuesday praised House Speaker Kevin McCarthy's new effort to educate Congress on artificial intelligence (AI), but predicted it may be some time until a substantial number of representatives become well-versed on the topic. Speaking with Fox News Digital at the Milken Institute Global Conference, Issa admitted that while he may not know nearly enough to be considered an expert on AI, he has been growing alongside technological advances that have cropped up across his time in business, military and legislation. "I've been able to grow with it and I'm not really nearly where I need to be. And that's why Speaker McCarthy has formalized the education using MIT and other organizations to educate members of Congress. And he's holding forums he's not requiring, but he's encouraging in a very, very explicit way that we all get educated enough to be part of the solution and not be taken out of fear or lack of knowledge," Issa said.

In September, San Diego robotics startup Brain Corporation will introduce artificial intelligence software that allows giant commercial floor-cleaning machines to navigate autonomously. The follow-up offering it wants to develop may be even more forward-looking: A training and certification program for janitors to operate the machines. The program, still in early stages of planning, is aimed at helping janitors maximize efficiency and establishing standards and best practices for the use of robots in janitorial work, according to Brain Corporation. The company says it is not aware any other such training program exists. There's additional incentive for Brain Corp. to offer training options.

In September, San Diego robotics startup Brain Corporation will introduce artificial intelligence software that allows giant commercial floor-cleaning machines to navigate autonomously. The follow-up offering it wants to develop may be even more forward-looking: A training and certification program for janitors to operate the machines. The program, still in early stages of planning, is aimed at helping janitors maximize efficiency and establishing standards and best practices for the use of robots in janitorial work, according to Brain Corporation. The company says it is not aware any other such training program exists. There's additional incentive for Brain Corp. to offer training options.