DISentangled Counterfactual Visual interpretER (DISCOVER) generalizes to natural images
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Abstract (arXiv)
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.
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References · 4
- 2014Deep Learning Face Attributes in the Wildcited by 3,693
- 2019StarGAN v2: Diverse Image Synthesis for Multiple Domainscited by 1,098
- 2015Adversarial Autoencoderscited by 364
- 2020Improving Image Autoencoder Embeddings with Perceptual Losscited by 13
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