  Open-set recognition (OSR) aims to simultaneously detect unknown-class
samples and classify known-class samples. Most of the existing OSR methods are
inductive methods, which generally suffer from the domain shift problem that
the learned model from the known-class domain might be unsuitable for the
unknown-class domain. Addressing this problem, inspired by the success of
transductive learning for alleviating the domain shift problem in many other
visual tasks, we propose an Iterative Transductive OSR framework, called
IT-OSR, which implements three explored modules iteratively, including a
reliability sampling module, a feature generation module, and a baseline update
module. Specifically, at each iteration, a dual-space consistent sampling
approach is presented in the explored reliability sampling module for selecting
some relatively more reliable ones from the test samples according to their
pseudo labels assigned by a baseline method, which could be an arbitrary
inductive OSR method. Then, a conditional dual-adversarial generative network
under an orthogonal coding condition is designed in the feature generation
module to generate discriminative sample features of both known and unknown
classes according to the selected test samples with their pseudo labels.
Finally, the baseline method is updated for sample re-prediction in the
baseline update module by jointly utilizing the generated features, the
selected test samples with pseudo labels, and the training samples. Extensive
experimental results on both the standard-dataset and the cross-dataset
settings demonstrate that the derived transductive methods, by introducing two
typical inductive OSR methods into the proposed IT-OSR framework, achieve
better performances than 15 state-of-the-art methods in most cases.
