Anomaly detection3 Deep One-Class Classification. ICML. 2018. (2/2) DSVDD: Deep Support Vector Data Description 3. Properties of Deep SVDD Proposition 1: All-zero-weights solution. Let \(\mathcal{W}_{0}\) be the set of all-zero network weights, i.e., \(\boldsymbol{W}^{l} = \boldsymbol{0}\) for every \(\boldsymbol{W}^{l} \in \mathcal{W}_{0}\). For this choice of parameters, the network maps any input to the same output, i.e., \(\phi(\boldsymbol{x};\mathcal{W}_{0}.. 2021. 7. 5. Deep One-Class Classification. ICML. 2018. (1/2) DSVDD: Deep Support Vector Data Description 1. Background 1.1 Kernel-based One-Class Classification. Let \(\mathcal{X} \subset \mathbb{R}^d\) be the data space. Let \( k : \mathcal{X} \times \mathcal{X} \rightarrow [0, \inf)\) be a PSD kernel, \(\mathcal{F}_{k}\) it's associated RKHS, and \(\phi_{k} : \mathcal{X} \rightarrow \mathcal{F}_{k}\) its associated feature mapping. So \(k(\boldsymbol{x}.. 2021. 6. 29. Explainable Deep One-Class Classification. ICLR. 2021 FCDD: Fully Convolutional Data Description 1. Background 1.1 Deep One-Class Classification. It performs anomaly detection by learing a neural network to map nomial samples near a center \(\textbf{c}\) in output space, causing anomalies to be mapped away. Hypersphere Classifier (HSC) is used for semi-supervised one-class classification like DSVDD (Ruff et al., 2018). HSC objective function : $$ \.. 2021. 6. 23. 이전 1 다음
