FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks
Abstract
With a constant improvement in the network architectures and training methodologies, Neural Networks (NNs) are increasingly being deployed in real-world Machine Learning systems. However, despite their impressive performance on known inputs, these NNs can fail absurdly on the unseen inputs, especially if these real-time inputs deviate from the training dataset distributions, or contain certain types of input noise. This indicates the low noise...
Paper Details
Title
FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks
Published Date
Dec 3, 2019
Journal
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