计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 220800218-6.doi: 10.11896/jsjkx.220800218
袁振, 刘进锋
YUAN Zhen, LIU Jinfeng
摘要： 垃圾分类是保护环境,提高资源利用率的有效措施。近年来,深度学习以其引入强大的建模能力在各个领域都有成功的表现,利用深度学习进行垃圾分类也成为一个新兴方向。垃圾分类数据集大多存在类别图片数量不均衡(长尾分布)的问题。针对垃圾分类中的长尾数据集TrashNet,提出了一种基于因果推理分类方法。该方法通过引入因果推理找出由输入样本引起的直接因果效应,缓解了TrashNet数据集的长尾问题。在训练中采用了迁移学习的方法,减少了训练参数量,并在其中使用了因果干预与反事实推理,进行了去混淆训练。对所提出的方法进行了类激活图(Class Activation Map)验证,实验结果表明,所提出的模型有较好的特征提取能力。模型对TrashNet数据集中的尾部类有较优的识别效果,并在其上取得了94.23%的最好精度。
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