Identifying Spurious Correlations and Correcting them with an Explanation-based Learning

Understand

Identifying spurious correlations learned by a trained model is at the core of refining a trained model and building a trustworthy model.

• We present a simple method to identify spurious correlations that have been learned by a model trained for image classification problems.
• We apply image-level perturbations and monitor changes in certainties of predictions made using the trained model.
• We demonstrate this approach using an image classification dataset that contains images with synthetically generated spurious regions and show that the trained model was overdependent on spurious regions.