The study delves into the concept of focus analysis in neural networks, specifically focusing on 3D point clouds. It introduces a refocusing algorithm to improve network robustness against corruptions and adversarial attacks. The research highlights the correlation between focus distribution and network performance, providing insights into enhancing classification accuracy while maintaining robustness.
Recent studies have shown that overfocusing can lead to less stable performance and reduced robustness when faced with changes in statistics during training. The proposed refocusing algorithm aims to align the focus distribution by filtering out influential input elements, resulting in improved network stability and resilience against out-of-distribution corruptions.
The study demonstrates the effectiveness of the refocusing approach through experiments on ModelNet-C dataset for zero-shot classification tasks and adversarial defense against Shape-Invariant attacks. Results show state-of-the-art performance in terms of robust classification and defense strategies for 3D point cloud networks.
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by Meir Yossef ... um arxiv.org 03-13-2024
https://arxiv.org/pdf/2308.05525.pdfTiefere Fragen