Hypothesis-Driven Deep Learning for Out of Distribution Detection
Yasith Jayawardana Azeem Ahmad Balpreet S. Ahluwalia Rafi Ahmad Sampath Jayarathna Dushan N. Wadduwage
Predictions of opaque black-box systems are frequently deployed in
high-stakes applications such as healthcare. For such applications, it is
crucial to assess how models handle samples beyond the domain of training data.
While several metrics and tests exist to detect out-of-distribution (OoD) data
from in-distribution (InD) data to a deep neural network (DNN), their
performance varies significantly across datasets, models, and tasks, which
limits their practical use. In this paper, we propose a hypothesis-driven
approach to quantify whether a new sample is InD or OoD. Given a trained DNN
and some input, we first feed the input through the DNN and compute an ensemble
of OoD metrics, which we term latent responses. We then formulate the OoD
detection problem as a hypothesis test between latent responses of different
groups, and use permutation-based resampling to infer the significance of the
observed latent responses under a null hypothesis. We adapt our method to
detect an unseen sample of bacteria to a trained deep learning model, and show
that it reveals interpretable differences between InD and OoD latent responses.
Our work has implications for systematic novelty detection and informed
decision-making from classifiers trained on a subset of labels.