Variational Autoencoders

We propose ARN, a semisupervised anomaly detection and generation method based on adversarial reconstruction. ARN exploits a regularized autoencoder to optimize the reconstruction of variants of normal examples with minimal differences, that are …

The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two-step …

In what follows I'll try to explain my basic understanding and interepretation of the semi-supervised framework based on Variational Autoencoders, as described in [1]. I shall assume a vector notation where bold symbols $\mathbf{a}$ represent vectors, whose $j$-th component can be represented as $a_j$. The starting point of the framework is to consider a dataset \(D = S \cup U\), where: \(S = \{(\mathbf{x}_1, \mathbf{y}_1), \ldots, (\mathbf{x}_n, \mathbf{y}_n)\}\), \(U = \{\mathbf{x}_{n+1}, \ldots, \mathbf{x}_{n+m}\}\), with \(\mathbf{x}_i \in \mathbb{R}^N\) and \(\mathbf{y}_i \in \{0,1\}^C\) represents a one-hot encoding of a class in \(\{1, \ldots, C\}\).

Variational autoencoders were proven successful in domains such as computer vision and speech processing. Their adoption for modeling user preferences is still unexplored, although recently it is starting to gain attention in the current literature. …