论文标题
强大图像分类的策略
Strategies for Robust Image Classification
论文作者
论文摘要
在这项工作中，我们评估了数字变化图像对人工神经网络性能的影响。我们探讨了对图像分类模型产生一致和准确结果的能力产生负面影响的因素。由于数字异常或物理环境的变化，模型的分类能力会受到对图像的变化的负面影响。本文的重点是发现和复制场景，以修改图像的外观并在最先进的机器学习模型上进行评估。我们的贡献提出了各种培训技术，可以增强模型对这些改变的鲁棒性的推广和提高鲁棒性的能力。
In this work we evaluate the impact of digitally altered images on the performance of artificial neural networks. We explore factors that negatively affect the ability of an image classification model to produce consistent and accurate results. A model's ability to classify is negatively influenced by alterations to images as a result of digital abnormalities or changes in the physical environment. The focus of this paper is to discover and replicate scenarios that modify the appearance of an image and evaluate them on state-of-the-art machine learning models. Our contributions present various training techniques that enhance a model's ability to generalize and improve robustness against these alterations.
