Most current deep learning methods assume the same class distributions for
training and testing datasets. However, recognition of possible unknown class
samples, i.e., classes not included in training that appear in
testing, is common in the real world. This realistic problem is known as
open-set recognition (OSR), where a classifier is trained to not only
distinguish between known classes, but also to identify unknown classes as
“unseen”. However, current state-of-the-art OSR methods rely heavily on large
amounts of labeled training data, which are often not easily available in real
applications. In this paper, we propose a novel semi-supervised causal open set
recognition framework, which is motivated by the idea that generalized class and
sample attributes learned through both labeled and unlabeled data will allow for
the generation of more accurate counterfactuals, increasing the accuracy of
unseen and seen recognition. Based on the proposed framework, a novel
counterfactual contrastive loss is designed to increase the consistency of
counterfactual generation across labeled and unlabeled data. Extensive
experiments conducted across five datasets demonstrate that our method
outperforms state-of-the-art methods. We show that bridging the gap between
semi-supervised learning methods and causal-based generative models mark a
significant advancement in OSR by utilizing both limited amounts of expensive,
labeled data and a much larger, inexpensive collection of unlabeled data.