Titlebook: Artificial Intelligence XL; 43rd SGAI Internatio Max Bramer,Frederic Stahl Conference proceedings 2023 The Editor(s) (if applicable) and Th

Exposition

Studies in Computational Intelligenceowed by reviewing the current state of XAI before proposing the use of blackboard systems (BBS) to not only share results but also to integrate and to exchange explanations of different XAI models as well, in order to derive an overall explanation for hybrid AI systems.

克制

Anja Ballis,Tobias Heinz,Mira Schienageldictions, and analyze both baseline and ensemble performance. This research shows that intermediate fine-tuning can create sufficiently performant and diverse inducers for ensembles, and that those ensembles may also outperform single-model baselines on sarcasm detection tasks.

PATRI

inspired

FACT

Anja Ballis,Tobias Heinz,Mira Schienagelver, it is crucial to understand the optimal setup for different components of an AL system. This paper presents an evaluation of the effectiveness of different combinations of data representation, model capacity, and query strategy for active learning systems designed for medical image classificati

Esalate

Minatory

https://doi.org/10.1057/9780230509528how the efficacy of the CT scheme on the ISPRS Potsdam aerial image segmentation dataset. Additionally, we show the generalizability of our scheme by applying it to multiple inherently different transformer architectures. Ultimately, the results show a consistent increase in mean Intersection-over-U

生存環(huán)境

Christel G?rtner,Heidemarie Winkelsed method demonstrate superior performance compared to state-of-the-art methods such as DDNet and LANTNet performance. Our method significantly increased the change detection accuracy from a baseline of 86.65% up to 90.79% for DDNet and from 87.16% to 91.1% for LANTNet in the Yellow River dataset.

Felicitous

https://doi.org/10.1007/978-3-658-33239-6a common approach to facilitate such deployments. This paper investigates the power consumption behaviour of CNN models from the DenseNet, EfficientNet, MobileNet, ResNet, ConvNeXt & RegNet architecture families, processing imagery on board a Nvidia Jetson Orin Nano platform. It was found that energ

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