Introspective Learning : A Two-Stage Approach for Inference in Neural Networks

• URL: http://arxiv.org/abs/2209.08425v1
• Date: Sat, 17 Sep 2022 23:31:03 GMT
• Title: Introspective Learning : A Two-Stage Approach for Inference in Neural Networks
• Authors: Mohit Prabhushankar, Ghassan AlRegib
• Abstract summary: We advocate for two stages in a neural network's decision making process. The first is the existing feed-forward inference framework where patterns in given data are sensed and associated with previously learned patterns. The second is a slower reflection stage where we ask the network to reflect on its feed-forward decision by considering and evaluating all available choices.
• Score: 18.32369721322249
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we advocate for two stages in a neural network's decision making process. The first is the existing feed-forward inference framework where patterns in given data are sensed and associated with previously learned patterns. The second stage is a slower reflection stage where we ask the network to reflect on its feed-forward decision by considering and evaluating all available choices. Together, we term the two stages as introspective learning. We use gradients of trained neural networks as a measurement of this reflection. A simple three-layered Multi Layer Perceptron is used as the second stage that predicts based on all extracted gradient features. We perceptually visualize the post-hoc explanations from both stages to provide a visual grounding to introspection. For the application of recognition, we show that an introspective network is 4% more robust and 42% less prone to calibration errors when generalizing to noisy data. We also illustrate the value of introspective networks in downstream tasks that require generalizability and calibration including active learning, out-of-distribution detection, and uncertainty estimation. Finally, we ground the proposed machine introspection to human introspection for the application of image quality assessment.

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