InCoDe: Interpretable Compressed Descriptions For Image Generation
Armand Comas Aditya Chattopadhyay Feliu Formosa Changyu Liu Octavia Camps Rene Vidal
Generative models have been successfully applied in diverse domains, from natural language processing to image synthesis. However, despite this success, a key challenge that remains is the ability to control the semantic content of the scene being generated. We argue that adequate control of the generation process requires a data representation that allows users to access and efficiently manipulate the semantic factors shaping the data distribution. This work advocates for the adoption of succinct, informative, and interpretable representations, quantified using information-theoretic principles. Through extensive experiments, we demonstrate the efficacy of our proposed framework both qualitatively and quantitatively. Our work contributes to the ongoing quest to enhance both controllability and interpretability in the generation process. Code available at github.com/ArmandCom/InCoDe.
Controllable Generation, Image Generation, Interpretability, Information Pursuit, Information Theory, Diffusion Models
We present a framework to enable image generation through succinct, informative, and interpretable descriptions of images.