On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection
Sangha Park Jisoo Mok Dahuin Jung Saehyung Lee Sungroh Yoon
Successful detection of Out-of-Distribution (OoD) data is becoming
increasingly important to ensure safe deployment of neural networks. One of the
main challenges in OoD detection is that neural networks output overconfident
predictions on OoD data, make it difficult to determine OoD-ness of data solely
based on their predictions. Outlier exposure addresses this issue by
introducing an additional loss that encourages low-confidence predictions on
OoD data during training. While outlier exposure has shown promising potential
in improving OoD detection performance, all previous studies on outlier
exposure have been limited to utilizing visual outliers. Drawing inspiration
from the recent advancements in vision-language pre-training, this paper
venture out to the uncharted territory of textual outlier exposure. First, we
uncover the benefits of using textual outliers by replacing real or virtual
outliers in the image-domain with textual equivalents. Then, we propose various
ways of generating preferable textual outliers. Our extensive experiments
demonstrate that generated textual outliers achieve competitive performance on
large-scale OoD and hard OoD benchmarks. Furthermore, we conduct empirical
analyses of textual outliers to provide primary criteria for designing
advantageous textual outliers: near-distribution, descriptiveness, and
inclusion of visual semantics.