@bank2019polar] are two other methods that have been used to reduce network size and computation load. These methods improve performance at the hardware level by reducing the size of weights at the binary code level. However, the standard GPU implementation remains challenging for these methods to achieve runtime speedup [@han2015learning]. Also, the advantages of these methods over other one-stage methods without the fully connected layers (the network pruning target in [@han2015deep]) is not clear.

**Domain Adaptation:** Object detection in the real world still needs to address challenges such as low image quality, large variance in the backgrounds, illumination variation, etc. These could lead to a significant domain shift between the training, validation and test data. Consequently, the field of domain adaptation has been widely studied in image classification [@tzeng2014deep; @lu2017unsupervised] and object detection [@chen2018domain; @dai2018dark] tasks. These methods improve accuracy on well-known bench-marking datasets. Nevertheless, they typically adopt an offline domain adaptation procedure and do not concern with domain-change during the inference stage.

**Knowledge Distillation:** Knowledge distillation is another approach to boost accuracy in CNNs. Under the knowledge distillation setting, an ensemble of CNN models or a very deep model will serve as the teacher model, which transfers its knowledge 