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An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines

Machine-learned safety-critical systems need to be self-aware and reliably know their unknowns in the open-world. This is often explored through the lens of anomaly/outlier detection or out-of-distribution modeling. One popular formulation is that of open-set classification, where an image classifier trained for 1-of-$K$ classes should also recognize images belonging to a $(K+1)^{th}$ "other" class, not present in the training set. Recent work has shown that, somewhat surprisingly, most if not all existing open-world methods do not work well on high-dimensional open-world images (Shafaei et al., 2019). In this paper, we carry out an empirical exploration of open-set classification, and find that combining classic statistical methods with carefully computed features can dramatically outperform prior work. We extract features from off-the-shelf (OTS) state-of-the-art (SOTA) networks for the underlying $K$-way closed-world task. We leverage insights from the retrieval community for computing feature descriptors that are low-dimensional (via pooling and PCA) and normalized (via L2-normalization), enabling the modeling of training data densities via classic statistical tools such as kmeans and Gaussian Mixture Models (GMMs). Finally, we (re)introduce the task of open-set semantic segmentation, which requires classifying individual pixels into one of $K$ known classes or an "other" class. In this setting, our feature-based statistical models noticeably outperform prior open-world methods.

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