Autoencoder Watchdog Outlier Detection for Classifiers
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Abstract (arXiv)
Neural networks have often been described as black boxes. A generic neural network trained to differentiate between kittens and puppies will classify a picture of a kumquat as a kitten or a puppy. An autoencoder watch dog screens trained classifier/regression machine input candidates before processing, e.g. to first test whether the neural network input is a puppy or a kitten. Preliminary results are presented using convolutional neural networks and convolutional autoencoder watchdogs using MNIST images.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Oct 2020.
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Citing papers per year: 2021: 5.
5 papers.
- 2021Symbiotic Hybrid Neural Network Watchdog For Outlier Detectioncited by 2
- 2021Classification of Common Waveforms Including a Watchdog for Unknown Signalscited by 1
- 2021Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detectioncited by 1
- 2021Generatively Augmented Neural Network Watchdog for Image Classification Networkscited by 0
- 2021Open-set Classification of Common Waveforms Using A Deep Feed-forward Network and Binary Isolation Forest Modelscited by 0
- 2021Open-set Classification of Common Waveforms Using A Deep Feed-forward Network and Binary Isolation Forest Models1 Oct 2021
- 2021Generatively Augmented Neural Network Watchdog for Image Classification Networks7 Sep 2021
- 2021Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detection20 Aug 2021
- 2021Classification of Common Waveforms Including a Watchdog for Unknown Signals16 Aug 2021
- 2021Symbiotic Hybrid Neural Network Watchdog For Outlier Detection28 Feb 2021