Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detection

TL;DR

This paper introduces a multi-tiered adversarial guard system called Cascade Watchdog, which leverages GAN-generated data and sequential guards to significantly improve out-of-distribution detection in neural networks.

Contribution

It proposes a novel cascade watchdog framework that enhances out-of-distribution detection by using GAN-generated data and a multi-stage adversarial training approach.

Findings

Significant improvement in detecting challenging out-of-distribution inputs

Maintains an extremely low false positive rate

Effective use of GAN data for adversarial training

Abstract

The identification of out-of-distribution content is critical to the successful implementation of neural networks. Watchdog techniques have been developed to support the detection of these inputs, but the performance can be limited by the amount of available data. Generative adversarial networks have displayed numerous capabilities, including the ability to generate facsimiles with excellent accuracy. This paper presents and empirically evaluates a multi-tiered watchdog, which is developed using GAN generated data, for improved out-of-distribution detection. The cascade watchdog uses adversarial training to increase the amount of available data similar to the out-of-distribution elements that are more difficult to detect. Then, a specialized second guard is added in sequential order. The results show a solid and significant improvement on the detection of the most challenging out-of-distribution inputs while preserving an extremely low false positive rate.