Abstract
Class activation maps (CAMs) explain convolutional neural network predictions by identifying salient pixels, but they become misaligned and misleading when explaining predictions on images under bias, such as images blurred accidentally or deliberately for privacy protection, or images with improper white balance. Despite model fine-tuning to improve prediction performance on these biased images, we demonstrate that CAM explanations become more deviated and unfaithful with increased image bias. We present Debiased-CAM to recover explanation faithfulness across various bias types and levels by training a multi-input, multi-task model with auxiliary tasks for CAM and bias level predictions. With CAM as a prediction task, explanations are made tunable by retraining the main model layers and made faithful by self-supervised learning from CAMs of unbiased images. The model provides representative, bias-agnostic CAM explanations about the predictions on biased images as if generated from their unbiased form. In four simulation studies with different biases and prediction tasks, Debiased-CAM improved both CAM faithfulness and task performance. We further conducted two controlled user studies to validate its truthfulness and helpfulness, respectively. Quantitative and qualitative analyses of participant responses confirmed Debiased-CAM as more truthful and helpful. Debiased-CAM thus provides a basis to generate more faithful and relevant explanations for a wide range of real-world applications with various sources of bias.
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URL
https://arxiv.org/abs/2012.05567
