Brain-Inspired Domain-Incremental Adaptive Detection for Autonomous Driving - Liang, Weihao; Gan, Lu; Wang, Pengfei; Meng, Wei (Frontiers in Neurorobotics, 2022)
Most of existing methods for unsupervised domain adaptation (UDA) only involve two domains, i.e., source domain and target domain. However, such trained adaptive models have poor performance when applied to a new domain without learning. Moreover, using UDA methods to adapt from the source domain to the new domains will lead to catastrophic forgetting on the previous target domain. To handle these issues, in this paper we propose a new incremental learning framework for domain-incremental cases, which can harmonize the memorability and discriminability of the existing and the novel domains. To evaluate the effectiveness of the proposed methods, we conduct two groups of experiments, including virtual-to-real and diverse-weather cases. The experimental results demonstrate that our approach can avoid catastrophic forgetting, mitigate performance degradation on the previous domains, and improve object detection accuracy significantly.