论文标题
归一化标签分布：学习校准，适应性和有效的激活图
Normalized Label Distribution: Towards Learning Calibrated, Adaptable and Efficient Activation Maps
论文作者
论文摘要
模型对数据畸变和对抗性攻击的脆弱性会影响其有效地划界不同类别边界的能力。该网络的信心和不确定性在体重调整以及承认此类攻击的程度中起着关键作用。在本文中，我们解决了分类网络的准确性和校准潜力之间的权衡。我们研究了基础真相分布对各种最先进网络的性能和概括性的重要性，并比较了所提出的方法对意外攻击的反应。此外，我们通过提出归一化的软标签来增强特征图的校准，证明了标签平滑的正则化和归一化在产生更好的概括性和校准概率分布中的作用。随后，我们通过将常规卷积转化为基于填充的部分卷积来证实我们的推论，以确立校正在增强性能和收敛速率方面的切实影响。我们以图形方式阐明了这种变化的含义，其关键目的是证实多个数据集的可靠性和可重复性。
The vulnerability of models to data aberrations and adversarial attacks influences their ability to demarcate distinct class boundaries efficiently. The network's confidence and uncertainty play a pivotal role in weight adjustments and the extent of acknowledging such attacks. In this paper, we address the trade-off between the accuracy and calibration potential of a classification network. We study the significance of ground-truth distribution changes on the performance and generalizability of various state-of-the-art networks and compare the proposed method's response to unanticipated attacks. Furthermore, we demonstrate the role of label-smoothing regularization and normalization in yielding better generalizability and calibrated probability distribution by proposing normalized soft labels to enhance the calibration of feature maps. Subsequently, we substantiate our inference by translating conventional convolutions to padding based partial convolution to establish the tangible impact of corrections in reinforcing the performance and convergence rate. We graphically elucidate the implication of such variations with the critical purpose of corroborating the reliability and reproducibility for multiple datasets.
