Existing approaches for semi-supervised object detection assume a fixed set
of classes present in training and unlabeled datasets, i.e., in-distribution
(ID) data. The performance of these techniques significantly degrades when
these techniques are deployed in the open-world, due to the fact that the
unlabeled and test data may contain objects that were not seen during training,
i.e., out-of-distribution (OOD) data. The two key questions that we explore in
this paper are: can we detect these OOD samples and if so, can we learn from
them? With these considerations in mind, we propose the Open World
Semi-supervised Detection framework (OWSSD) that effectively detects OOD data
along with a semi-supervised learning pipeline that learns from both ID and OOD
data. We introduce an ensemble based OOD detector consisting of lightweight
auto-encoder networks trained only on ID data. Through extensive evalulation,
we demonstrate that our method performs competitively against state-of-the-art
OOD detection algorithms and also significantly boosts the semi-supervised
learning performance in open-world scenarios.

在这篇论文中，我们提出了一个名为 Open World Semi-supervised Detection（OWSSD）的框架，该框架通过一种轻量级的自编码器网络对进行过 ID 数据训练从而有效地检测 OOD 数据，并从中学习，我们通过大量评估表明我们的方法在与最先进的 OOD 检测算法的竞争中表现出色，并显著改善了开放世界场景下的半监督学习性能。