  Open set recognition (OSR) requires the model to classify samples that belong
to closed sets while rejecting unknown samples during test. Currently,
generative models often perform better than discriminative models in OSR, but
recent studies show that generative models may be computationally infeasible or
unstable on complex tasks. In this paper, we provide insights into OSR and find
that learning supplementary representations can theoretically reduce the open
space risk. Based on the analysis, we propose a new model, namely Multi-Expert
Diverse Attention Fusion (MEDAF), that learns diverse representations in a
discriminative way. MEDAF consists of multiple experts that are learned with an
attention diversity regularization term to ensure the attention maps are
mutually different. The logits learned by each expert are adaptively fused and
used to identify the unknowns through the score function. We show that the
differences in attention maps can lead to diverse representations so that the
fused representations can well handle the open space. Extensive experiments are
conducted on standard and OSR large-scale benchmarks. Results show that the
proposed discriminative method can outperform existing generative models by up
to 9.5% on AUROC and achieve new state-of-the-art performance with little
computational cost. Our method can also seamlessly integrate existing
classification models. Code is available at https://github.com/Vanixxz/MEDAF.
