Semantic Predictive Coding with Arbitrated Generative Adversarial Networks - Radamanthys Stivaktakis; Grigorios Tsagkatakis; Panagiotis Tsakalides (Machine Learning and Knowledge Extraction, 2020)
In spatio-temporal predictive coding problems, like next-frame prediction in video, determining the content of plausible future frames is primarily based on the image dynamics of previous frames. We establish an alternative approach based on their underlying semantic information when considering data that do not necessarily incorporate a temporal aspect, but instead they comply with some form of associative ordering. In this work, we introduce the notion of semantic predictive coding by proposing a novel generative adversarial modeling framework which incorporates the arbiter classifier as a new component. While the generator is primarily tasked with the anticipation of possible next frames, the arbiter’s principal role is the assessment of their credibility. Taking into account that the denotative meaning of each forthcoming element can be encapsulated in a generic label descriptive of its content, a classification loss is introduced along with the adversarial loss. As supported by our experimental findings in a next-digit and a next-letter scenario, the utilization of the arbiter not only results in an enhanced GAN performance, but it also broadens the network’s creative capabilities in terms of the diversity of the generated symbols.