TDLS: A Top-Down Layer Searching Algorithm for Generating Counterfactual Visual Explanation
This addresses the need for human-friendly explanations in AI for improving transparency and fairness, though it is incremental as it adapts counterfactual methods to a specific domain.
The paper tackles the problem of generating counterfactual visual explanations for fine-grained image classification, demonstrating that their TDLS algorithm provides more flexible explanations efficiently using a VGG-16 model on the Caltech-UCSD Birds 200 dataset.
Explanation of AI, as well as fairness of algorithms' decisions and the transparency of the decision model, are becoming more and more important. And it is crucial to design effective and human-friendly techniques when opening the black-box model. Counterfactual conforms to the human way of thinking and provides a human-friendly explanation, and its corresponding explanation algorithm refers to a strategic alternation of a given data point so that its model output is "counter-facted", i.e. the prediction is reverted. In this paper, we adapt counterfactual explanation over fine-grained image classification problem. We demonstrated an adaptive method that could give a counterfactual explanation by showing the composed counterfactual feature map using top-down layer searching algorithm (TDLS). We have proved that our TDLS algorithm could provide more flexible counterfactual visual explanation in an efficient way using VGG-16 model on Caltech-UCSD Birds 200 dataset. At the end, we discussed several applicable scenarios of counterfactual visual explanations.