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.

翻译：开放集识别（OSR）要求模型在测试阶段对属于封闭集的样本进行分类，同时拒绝未知样本。当前，生成模型在OSR中通常优于判别模型，但近期研究表明，生成模型在复杂任务上可能存在计算不可行或不稳定问题。本文通过剖析OSR发现，学习补充表征理论上可降低开放空间风险。基于此分析，我们提出新模型——多专家多样化注意力融合（MEDAF），以判别方式学习多样化表征。MEDAF由多个专家构成，通过注意力多样性正则化项强制各专家注意力图互异。每个专家学得的逻辑值经自适应融合后，借助得分函数识别未知样本。我们证明注意力图的差异可产生多样化表征，从而使得融合表征能有效应对开放空间。在标准及大规模OSR基准上进行的广泛实验表明，本判别方法在AUROC指标上最高可超越现有生成模型9.5%，并以极低计算开销刷新最优性能。本方法还可无缝集成现有分类模型。代码开源地址：https://github.com/Vanixxz/MEDAF。