  Semi-supervised learning (SSL) methods effectively leverage unlabeled data to
improve model generalization. However, SSL models often underperform in
open-set scenarios, where unlabeled data contain outliers from novel categories
that do not appear in the labeled set. In this paper, we study the challenging
and realistic open-set SSL setting, where the goal is to both correctly
classify inliers and to detect outliers. Intuitively, the inlier classifier
should be trained on inlier data only. However, we find that inlier
classification performance can be largely improved by incorporating
high-confidence pseudo-labeled data, regardless of whether they are inliers or
outliers. Also, we propose to utilize non-linear transformations to separate
the features used for inlier classification and outlier detection in the
multi-task learning framework, preventing adverse effects between them.
Additionally, we introduce pseudo-negative mining, which further boosts outlier
detection performance. The three ingredients lead to what we call Simple but
Strong Baseline (SSB) for open-set SSL. In experiments, SSB greatly improves
both inlier classification and outlier detection performance, outperforming
existing methods by a large margin. Our code will be released at
https://github.com/YUE-FAN/SSB.
