论文标题
迈向ML生物多样性方法：一种新型的野生蜜蜂数据集和对ML辅助稀有物种注释的XAI方法的评估
Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species Annotations
论文作者
论文摘要
昆虫是我们生态系统的关键部分。可悲的是，在过去的几十年中，他们的人数令人担忧。为了更好地了解这一过程并监测昆虫的种群，深度学习可能会提供可行的解决方案。但是，鉴于其分类法的广度和典型的细粒度分析障碍，例如与低类间变异性相比，较高的类内变异性，昆虫分类仍然是一项艰巨的任务。很少有基准数据集，这阻碍了更好的AI模型的快速发展。但是，稀有物种培训数据的注释需要专家知识。可解释的人工智能（XAI）可以协助生物学家执行这些注释任务，但是选择最佳XAI方法很难。我们对这些研究挑战的贡献是三个方面：1）从Inaturist数据库中取样的野生蜜蜂图像的数据集，2）在野生蜜蜂数据集中训练的重新网络模型，可在野生蜜蜂数据集上获得与其他精细数据集中培训的类似的先进模型相当的分类分数，并调查了XAI方法的其他良好元模型。
Insects are a crucial part of our ecosystem. Sadly, in the past few decades, their numbers have worryingly decreased. In an attempt to gain a better understanding of this process and monitor the insects populations, Deep Learning may offer viable solutions. However, given the breadth of their taxonomy and the typical hurdles of fine grained analysis, such as high intraclass variability compared to low interclass variability, insect classification remains a challenging task. There are few benchmark datasets, which impedes rapid development of better AI models. The annotation of rare species training data, however, requires expert knowledge. Explainable Artificial Intelligence (XAI) could assist biologists in these annotation tasks, but choosing the optimal XAI method is difficult. Our contribution to these research challenges is threefold: 1) a dataset of thoroughly annotated images of wild bees sampled from the iNaturalist database, 2) a ResNet model trained on the wild bee dataset achieving classification scores comparable to similar state-of-the-art models trained on other fine-grained datasets and 3) an investigation of XAI methods to support biologists in annotation tasks.
