We examine whether data generated by explanation techniques, which promote a process of self-reflection, can improve classifier performance. Our work is based on the idea that humans have the ability to make quick, intuitive decisions as well as to reflect on their own thinking and learn from explanations. To the best of our knowledge, this is the first time that the potential of mimicking this process by using explanations generated by explainability methods has been explored. We found that combining explanations with traditional labeled data leads to significant improvements in classification accuracy and training efficiency across multiple image classification datasets and convolutional neural network architectures. It is worth noting that during training, we not only used explanations for the correct or predicted class, but also for other classes. This serves multiple purposes, including allowing for reflection on potential outcomes and enriching the data through augmentation.
PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Maryland（马里兰大学）；Cardiff University（卡迪夫大学）；RWTH Aachen University（亚琛工业大学）
Towards Robust Explanations for Deep Neural Networks
Technische Universität Berlin（柏林工业大学）；BIFOLD - Berlin Institute for the Foundations of Learning and Data（BIFOLD——柏林学习与数据基础研究所）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Google Research（谷歌研究院）
Institute of Economics and L’EMbEDS, Sant’Anna School of Advanced Studies（经济学研究所和L’EMbEDS，圣安娜高级研究院）；Dept. of Statistics and Huck Institutes of the Life Sciences, Penn State University（统计系和生命科学学院，宾夕法尼亚州立大学）
