Denoising Autoencoders for Overgeneralization in Neural Networks.

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ID: 61048
2019
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Ranked #2 of 23 articles by views in ieee transactions on pattern analysis and machine intelligence

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Abstract
Despite recent developments that allowed neural networks to achieve impressive performance on a variety of applications, these models are intrinsically affected by the problem of overgeneralization, due to their partitioning of the full input space into the fixed set of target classes used during training. Thus it is possible for novel inputs belonging to categories unknown during training or even completely unrecognizable to humans to fool the system into classifying them as one of the known classes, even with a high degree of confidence. This problem can lead to security problems in critical applications, and is closely linked to open set recognition and 1-class recognition. This paper presents a novel way to compute a confidence score using the reconstruction error of denoising autoencoders and shows how it can correctly identify the regions of the input space close to the training distribution. The proposed solution is tested on benchmarks of fooling, open set recognition and 1-class recognition constructed from the MNIST and Fashion-MNIST datasets.
Reference Key
spigler2019denoisingieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Spigler, Giacomo;
Journal ieee transactions on pattern analysis and machine intelligence
Year 2019
DOI
10.1109/TPAMI.2019.2909876
URL
Keywords
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