论文标题
带有高斯混合物的开放式识别变量自动编码器
Open-Set Recognition with Gaussian Mixture Variational Autoencoders
论文作者
论文摘要
在推论中，开放设定的分类是将样本分类为已知类别，或拒绝将其拒绝为未知类别。现有的深层开放式分类器训练明确的封闭设定分类器，在某些情况下，使用重建不一致，我们发现这稀释了潜在表示区分未知类别的能力。相比之下，我们训练模型以合作学习重建并在潜在空间中执行基于班级的聚类。这样，我们的高斯混合物变化自动编码器（GMVAE）通过通过分析结果的大量实验来实现更准确和健壮的开放式分类结果，平均F1提高了29.5％。
In inference, open-set classification is to either classify a sample into a known class from training or reject it as an unknown class. Existing deep open-set classifiers train explicit closed-set classifiers, in some cases disjointly utilizing reconstruction, which we find dilutes the latent representation's ability to distinguish unknown classes. In contrast, we train our model to cooperatively learn reconstruction and perform class-based clustering in the latent space. With this, our Gaussian mixture variational autoencoder (GMVAE) achieves more accurate and robust open-set classification results, with an average F1 improvement of 29.5%, through extensive experiments aided by analytical results.
