Understanding Character Recognition using Visual Explanations Derived from the Human Visual System and Deep Networks

• URL: http://arxiv.org/abs/2108.04558v1
• Date: Tue, 10 Aug 2021 10:09:37 GMT
• Title: Understanding Character Recognition using Visual Explanations Derived from the Human Visual System and Deep Networks
• Authors: Chetan Ralekar, Shubham Choudhary, Tapan Kumar Gandhi, Santanu Chaudhury
• Abstract summary: We examine the congruence, or lack thereof, in the information-gathering strategies of deep neural networks. The deep learning model considered similar regions in character, which humans have fixated in the case of correctly classified characters. We propose to use the visual fixation maps obtained from the eye-tracking experiment as a supervisory input to align the model's focus on relevant character regions.
• Score: 6.734853055176694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human observers engage in selective information uptake when classifying visual patterns. The same is true of deep neural networks, which currently constitute the best performing artificial vision systems. Our goal is to examine the congruence, or lack thereof, in the information-gathering strategies of the two systems. We have operationalized our investigation as a character recognition task. We have used eye-tracking to assay the spatial distribution of information hotspots for humans via fixation maps and an activation mapping technique for obtaining analogous distributions for deep networks through visualization maps. Qualitative comparison between visualization maps and fixation maps reveals an interesting correlate of congruence. The deep learning model considered similar regions in character, which humans have fixated in the case of correctly classified characters. On the other hand, when the focused regions are different for humans and deep nets, the characters are typically misclassified by the latter. Hence, we propose to use the visual fixation maps obtained from the eye-tracking experiment as a supervisory input to align the model's focus on relevant character regions. We find that such supervision improves the model's performance significantly and does not require any additional parameters. This approach has the potential to find applications in diverse domains such as medical analysis and surveillance in which explainability helps to determine system fidelity.

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