Generative Visual Rationales
This addresses interpretability and data scarcity issues in domains like medicine, but it is incremental as it builds on existing semi-supervised and interpretability methods.
The paper tackles the problem of interpretability and limited labeled data in deep learning by proposing a semi-supervised technique that uses large unlabeled datasets to encode images into latent representations, applied to chest radiography to identify heart failure and generate visual rationales by optimizing latent vectors to produce disease-free images for interpretability.
Interpretability and small labelled datasets are key issues in the practical application of deep learning, particularly in areas such as medicine. In this paper, we present a semi-supervised technique that addresses both these issues by leveraging large unlabelled datasets to encode and decode images into a dense latent representation. Using chest radiography as an example, we apply this encoder to other labelled datasets and apply simple models to the latent vectors to learn algorithms to identify heart failure. For each prediction, we generate visual rationales by optimizing a latent representation to minimize the prediction of disease while constrained by a similarity measure in image space. Decoding the resultant latent representation produces an image without apparent disease. The difference between the original decoding and the altered image forms an interpretable visual rationale for the algorithm's prediction on that image. We also apply our method to the MNIST dataset and compare the generated rationales to other techniques described in the literature.