Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

節(jié)省

,Exploring the?Role of?Recursive Convolutional Layer in?Generative Adversarial Networks,ualitatively and quantitatively. Preliminary experiments suggest that the use of recursive layers holds significant potential to generate higher-quality samples in GANs. The code is publicly available at ..

發(fā)生

叢林

tenosynovitis

鞏固

,Low-Frequency Features Optimization for?Transferability Enhancement in?Radar Target Adversarial Attl examples focus on the low-frequency features of attacked targets, which are more generalized. The adversarial examples are guided to attack the high-level semantic features of the target, and the transferability of adversarial examples is improved. Experimental results on moving and stationary tar

legacy

Multi-convolution and Adaptive-Stride Based Transferable Adversarial Attacks,aptive-stride module adjusts the stride adaptively to control the change range of the stride. Experimental results have shown that MCAN-FGM has a higher?attack success rate?than state-of-the-art gradient-based attack methods.

反抗者

,Multi-source Open-Set Image Classification Based on?Deep Adversarial Domain Adaptation,ture space. Furthermore, to address the inadequate handling of unknown classes in existing methods, we further partition the unknown class samples in the target domain. The proposed model is evaluated on three datasets, and consistently outperforms baseline methods and benchmark single-source open-s

Accrue

吞沒

,Towards Robustness of?Large Language Models on?Text-to-SQL Task: An Adversarial and?Cross-Domain Inro-shot text-to-SQL parsers, their performances degrade under adversarial and domain generalization perturbations, with varying degrees of robustness depending on the type and level of perturbations applied. We also explore the impact of usage-related factors such as prompt design on the performance

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