Variational Encoder-based Reliable Classification

• URL: http://arxiv.org/abs/2002.08289v2
• Date: Sat, 17 Oct 2020 13:51:37 GMT
• Title: Variational Encoder-based Reliable Classification
• Authors: Chitresh Bhushan, Zhaoyuan Yang, Nurali Virani, Naresh Iyer
• Abstract summary: We propose an Epistemic (EC) that can provide justification of its belief using support from the training dataset as well as quality of reconstruction. Our approach is based on modified variational autoencoders that can identify a semantically meaningful low-dimensional space. Our results demonstrate improved reliability of predictions and robust identification of samples with adversarial attacks.
• Score: 5.161531917413708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning models provide statistically impressive results which might be individually unreliable. To provide reliability, we propose an Epistemic Classifier (EC) that can provide justification of its belief using support from the training dataset as well as quality of reconstruction. Our approach is based on modified variational auto-encoders that can identify a semantically meaningful low-dimensional space where perceptually similar instances are close in $\ell_2$-distance too. Our results demonstrate improved reliability of predictions and robust identification of samples with adversarial attacks as compared to baseline of softmax-based thresholding.

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