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Abstract:
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Neural Networks are very complicated and very useful models for image recognition, but they are generally used to recognize very complex and multifaceted stimuli, like cars or people. When neural networks are used to recognize simpler objects with overlapping feature sets, things can go a bit haywire. In this talk, we'll discuss a model built for applications in statistical forensics which uncovered some very interesting problems between model-based perception and human perception. I will show visual diagnostics which provide insight into the model, and talk about ways we might address the discrepancy between human perception and model perception to produce more accurate and useful model predictions.
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