Attention guidance is an approach to addressing dataset bias in deep learning, where the model relies on incorrect features to make decisions. Focusing on image classification tasks, we propose an efficient human-in-the-loop system to interactively direct the attention of classifiers to the regions specified by users, thereby reducing the influence of co-occurrence bias and improving the transferability and interpretability of a DNN. Previous approaches for attention guidance require the preparation of pixel-level annotations and are not designed as interactive systems. We present a new interactive method to allow users to annotate images with simple clicks, and study a novel active learning strategy to significantly reduce the number of annotations. We conducted both a numerical evaluation and a user study to evaluate the proposed system on multiple datasets. Compared to the existing non-active-learning approach which usually relies on huge amounts of polygon-based segmentation masks to fine-tune or train the DNNs, our system can save lots of labor and money and obtain a fine-tuned network that works better even when the dataset is biased. The experiment results indicate that the proposed system is efficient, reasonable, and reliable.
翻译:关注指导是解决深层学习中数据集偏差的一种方法,模型依赖不正确的特征来做出决策。我们以图像分类任务为重点,建议建立一个高效的 " 人到行 " 系统,将分类员的注意力交互式地引导到用户指定的区域,从而减少共同发生偏差的影响,并改进DNN的可转移性和可解释性。以往的注意指导方法需要编写像素级说明,而不是设计成互动系统。我们提出了一个新的互动方法,使用户能够用简单的点击对图像进行注释,并研究新的积极学习战略,以大幅减少说明的数量。我们进行了数字评价和用户研究,以评价多数据集的拟议系统。与现有的非主动学习方法相比,通常依赖大量多边分隔面的分隔面遮罩来微调或培训DNNN,我们的系统可以节省大量劳动力和金钱,并获得一个即使在数据集存在偏差的情况下也更好运作的微调网络。实验结果表明,拟议的系统是高效、合理和可靠的。