7 citations · 14 across the 7 of their papers we have counts for
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cs.CV2025★ 1 cited
Explaining Low Perception Model Competency with High-Competency Counterfactuals
Sara Pohland, Claire Tomlin
There exist many methods to explain how an image classification model generates its decision, but very little work has explored methods to explain why a classifier might lack confi…
cs.CV2024★ 1 cited
PaRCE: Probabilistic and Reconstruction-based Competency Estimation for CNN-based Image Classification
Sara Pohland, Claire Tomlin
Convolutional neural networks (CNNs) are extremely popular and effective for image classification tasks but tend to be overly confident in their predictions. Various works have sou…
cs.CV2024★ 4 cited
Understanding the Dependence of Perception Model Competency on Regions in an Image
Sara Pohland, Claire Tomlin
While deep neural network (DNN)-based perception models are useful for many applications, these models are black boxes and their outputs are not yet well understood. To confidently…