论文标题
朝着最接近示例的平均值的班级内科对象检测
Towards Class-incremental Object Detection with Nearest Mean of Exemplars
论文作者
论文摘要
增量学习是在线学习的一种形式。增量学习可以修改深度学习模型的参数和结构，以便模型在学习新知识时不会忘记旧知识。防止灾难性遗忘是增量学习的最重要任务。但是，当前的增量学习通常仅适用于一种输入。例如，如果输入图像是相同类型的，那么当前的增量模型可以学习新知识，而不会忘记旧知识。但是，如果将多个类别添加到输入图形中，则当前模型将无法正确处理，并且准确性将大大下降。因此，本文提出了一种增量方法，该方法通过识别原型向量并增加向量的距离来调整模型的参数，以便模型可以在不灾难性遗忘的情况下学习新知识。实验显示了我们提出的方法的有效性。
Incremental learning is a form of online learning. Incremental learning can modify the parameters and structure of the deep learning model so that the model does not forget the old knowledge while learning new knowledge. Preventing catastrophic forgetting is the most important task of incremental learning. However, the current incremental learning is often only for one type of input. For example, if the input images are of the same type, the current incremental model can learn new knowledge while not forgetting old knowledge. However, if several categories are added to the input graphics, the current model will not be able to deal with it correctly, and the accuracy will drop significantly. Therefore, this paper proposes a kind of incremental method, which adjusts the parameters of the model by identifying the prototype vector and increasing the distance of the vector, so that the model can learn new knowledge without catastrophic forgetting. Experiments show the effectiveness of our proposed method.
