Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris

TL;DR
This paper introduces BDSG, a novel invertible-residual-network-based model for generating samples on the boundary of data distributions, improving out-of-distribution detection and boundary approximation in complex, multimodal datasets.
Contribution
It proposes a new boundary generator using IResNet and ResFlow, addressing limitations of GANs in capturing distribution tails and multimodal supports for anomaly detection.
Findings
Effective boundary sample generation on synthetic and real datasets
Competitive out-of-distribution detection performance
Addresses non-convex, multimodal distribution supports
Abstract
Generative models, such as Generative Adversarial Networks (GANs), have been used for unsupervised anomaly detection. While performance keeps improving, several limitations exist particularly attributed to difficulties at capturing multimodal supports and to the ability to approximate the underlying distribution closer to the tails, i.e. the boundary of the distribution's support. This paper proposes an approach that attempts to alleviate such shortcomings. We propose an invertible-residual-network-based model, the Boundary of Distribution Support Generator (BDSG). GANs generally do not guarantee the existence of a probability distribution and here, we use the recently developed Invertible Residual Network (IResNet) and Residual Flow (ResFlow), for density estimation. These models have not yet been used for anomaly detection. We leverage IResNet and ResFlow for Out-of-Distribution (OoD)…
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