We present a novel model called OCGAN for the classical problem of one-class novelty detection, where, given a set of examples from a particular class, the goal is to determine if a query example is from the same class. Our solution is based on learning latent representations of in-class examples using a denoising auto-encoder network. The key contribution of our work is our proposal to explicitly constrain the latent space to exclusively represent the given class. In order to accomplish this goal, firstly, we force the latent space to have bounded support by introducing a tanh activation in the encoder's output layer. Secondly, using a discriminator in the latent space that is trained adversarially, we ensure that encoded representations of in-class examples resemble uniform random samples drawn from the same bounded space. Thirdly, using a second adversarial discriminator in the input space, we ensure all randomly drawn latent samples generate examples that look real. Finally, we introduce a gradient-descent based sampling technique that explores points in the latent space that generate potential out-of-class examples, which are fed back to the network to further train it to generate in-class examples from those points. The effectiveness of the proposed method is measured across four publicly available datasets using two one-class novelty detection protocols where we achieve state-of-the-art results.
翻译:我们提出了一个名为OCGAN的新模式,用于典型的单级新发现古老问题,根据一组特定类别的例子,我们提出一个名为OCGAN的新模式,其目标就是确定一个查询示例是否来自同一类。我们的解决办法是学习使用自定义自动编码器网络的分类内示例的潜在表现。我们工作的主要贡献是提出明确限制潜伏空间以只代表特定类的建议。为了实现这一目标,首先,我们通过在编码器输出层中引入土光激活来迫使潜潜藏空间获得约束性支持。第二,在经过对抗性培训的潜在空间中,我们使用一个歧视者,确保分类内示例的编码表达方式类似于从同一封闭空间中提取的统一随机样本。第三,在输入空间中使用第二个对称的对称区分器,我们确保所有随机抽取的潜伏样本都产生真实的范例。最后,我们引入了一种基于渐变白的采样技术,通过在生成可能的类外示例的潜在空间中找到点。第二,我们又反馈到网络中,以便进一步培训从同一封闭空间中采集的新型样本。