Abstract
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.
Abstract (translated)
针对单类新颖性检测的经典问题，我们提出了一个新的模型ocgan，在这个模型中，给定一组特定类的实例，目的是确定查询实例是否来自同一类。我们的解决方案是基于学习使用去噪自动编码器网络的类内示例的潜在表示。我们工作的主要贡献是我们的建议，明确限制潜在空间，专门代表特定的阶级。为了实现这一目标，我们首先通过在编码器的输出层引入TANH激活来强制潜在空间有界支持。其次，利用对潜在空间进行逆向训练的鉴别器，确保类内实例的编码表示与从同一有界空间提取的均匀随机样本相似。第三，在输入空间使用第二个敌方鉴别器，我们确保所有随机抽取的潜在样本生成看起来真实的示例。最后，我们介绍了一种基于梯度下降的采样技术，该技术探索潜在空间中产生类外电位的点，这些点反馈给网络，进一步训练网络从这些点生成类内电位的例子。该方法的有效性是通过使用两个一类新颖性检测协议的四个公开数据集来测量的，我们在这两个协议中实现了最先进的结果。
URL
https://arxiv.org/abs/1903.08550
