DISentangled Counterfactual Visual interpretER (DISCOVER) generalizes to natural images
This work extends an existing interpretability method to natural images, which is incremental but could benefit researchers and practitioners in computer vision.
The authors demonstrated that DISCOVER, a method for visual interpretability of image classification models, can be applied to natural images, successfully identifying semantic traits like nose size and facial characteristics to discriminate between dogs vs. cats and other facial features.
We recently presented DISentangled COunterfactual Visual interpretER (DISCOVER), a method toward systematic visual interpretability of image-based classification models and demonstrated its applicability to two biomedical domains. Here we demonstrate that DISCOVER can be applied to the domain of natural images. First, DISCOVER visually interpreted the nose size, the muzzle area, and the face size as semantic discriminative visual traits discriminating between facial images of dogs versus cats. Second, DISCOVER visually interpreted the cheeks and jawline, eyebrows and hair, and the eyes, as discriminative facial characteristics. These successful visual interpretations across two natural images domains indicate that DISCOVER is a generalized interpretability method.