Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detection
This addresses the critical need for reliable outlier detection in neural network implementations, though it appears incremental as it builds on existing watchdog and GAN techniques.
The paper tackled the problem of detecting out-of-distribution inputs in neural networks by proposing a multi-tiered watchdog using GAN-generated data, resulting in solid and significant improvement in detecting challenging outliers while maintaining an extremely low false positive rate.
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.