Double InfoGAN for Contrastive Analysis
Florence Carton Robin Louiset Pietro Gori
Contrastive Analysis (CA) deals with the discovery of what is common and what
is distinctive of a target domain compared to a background one. This is of
great interest in many applications, such as medical imaging. Current
state-of-the-art (SOTA) methods are latent variable models based on VAE
(CA-VAEs). However, they all either ignore important constraints or they don't
enforce fundamental assumptions. This may lead to sub-optimal solutions where
distinctive factors are mistaken for common ones (or viceversa). Furthermore,
the generated images have a rather poor quality, typical of VAEs, decreasing
their interpretability and usefulness. Here, we propose Double InfoGAN, the
first GAN based method for CA that leverages the high-quality synthesis of GAN
and the separation power of InfoGAN. Experimental results on four visual
datasets, from simple synthetic examples to complex medical images, show that
the proposed method outperforms SOTA CA-VAEs in terms of latent separation and
image quality. Datasets and code are available online.