Correcting Classification: A Bayesian Framework Using Explanation Feedback to Improve Classification Abilities

• URL: http://arxiv.org/abs/2105.02653v1
• Date: Thu, 29 Apr 2021 13:59:21 GMT
• Title: Correcting Classification: A Bayesian Framework Using Explanation Feedback to Improve Classification Abilities
• Authors: Yanzhe Bekkemoen, Helge Langseth
• Abstract summary: Explanations are social, meaning they are a transfer of knowledge through interactions. We overcome these difficulties by training a Bayesian convolutional neural network (CNN) that uses explanation feedback. Our proposed method utilizes this feedback for fine-tuning to correct the model such that the explanations and classifications improve.
• Score: 2.0931163605360115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural networks (NNs) have shown high predictive performance, however, with shortcomings. Firstly, the reasons behind the classifications are not fully understood. Several explanation methods have been developed, but they do not provide mechanisms for users to interact with the explanations. Explanations are social, meaning they are a transfer of knowledge through interactions. Nonetheless, current explanation methods contribute only to one-way communication. Secondly, NNs tend to be overconfident, providing unreasonable uncertainty estimates on out-of-distribution observations. We overcome these difficulties by training a Bayesian convolutional neural network (CNN) that uses explanation feedback. After training, the model presents explanations of training sample classifications to an annotator. Based on the provided information, the annotator can accept or reject the explanations by providing feedback. Our proposed method utilizes this feedback for fine-tuning to correct the model such that the explanations and classifications improve. We use existing CNN architectures to demonstrate the method's effectiveness on one toy dataset (decoy MNIST) and two real-world datasets (Dogs vs. Cats and ISIC skin cancer). The experiments indicate that few annotated explanations and fine-tuning epochs are needed to improve the model and predictive performance, making the model more trustworthy and understandable.

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