  Despite the data labeling cost for the object detection tasks being
substantially more than that of the classification tasks, semi-supervised
learning methods for object detection have not been studied much. In this
paper, we propose an Interpolation-based Semi-supervised learning method for
object Detection (ISD), which considers and solves the problems caused by
applying conventional Interpolation Regularization (IR) directly to object
detection. We divide the output of the model into two types according to the
objectness scores of both original patches that are mixed in IR. Then, we apply
a separate loss suitable for each type in an unsupervised manner. The proposed
losses dramatically improve the performance of semi-supervised learning as well
as supervised learning. In the supervised learning setting, our method improves
the baseline methods by a significant margin. In the semi-supervised learning
setting, our algorithm improves the performance on a benchmark dataset (PASCAL
VOC and MSCOCO) in a benchmark architecture (SSD).
