Exploring the Latent Visual Space Between Adjectives with Generative Adversarial Networks
Generative Adversarial Networks (GANs) have been applied for multiple cases, such as, generating images and image completion. One interesting feature of GANs is the exploration in the Latent Space, where new elements can appear caused by the interpolation between two seed elements. With this in mind, we are interested in exploring the latent space in terms of Adjective-Noun Pairs (ANP) able to capture subjectivity in visual content such as "cloudy sky" vs. "pretty sky". Although it is challenging for humans to find a smooth transition between two ANPs (similar to color gradient or color progression), the presented GANs are capable of generating such a gradient in the adjective domain and find new ANPs that lies in this (subjective) transition. As result, GANs offer a more quantified interpretation for this subjective progression and an explainability of the underlying latent space.
Dr. Damian Borth is the Director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, the Principle Investigator of the NVIDIA AI Lab at the DFKI, and founding co-director of Sociovestix Labs, a social enterprise in the area of financial data science. Damians research focuses on large-scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights (trends, sentiment) from online media streams.