{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T11:11:50Z","timestamp":1657019510644},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Realistic fine-grained multi-agent simulation of real-world complex systems is crucial for many downstream tasks such as reinforcement learning. Recent work has used generative models (GANs in particular) for providing high-fidelity simulation of real-world systems. However, such generative models are often monolithic and miss out on modeling the interaction in multi-agent systems. In this work, we take a first step towards building multiple interacting generative models (GANs) that reflects the interaction in real world. We build and analyze a hierarchical set-up where a higher-level GAN is conditioned on the output of multiple lower-level GANs. We present a technique of using feedback from the higher-level GAN to improve performance of lower-level GANs. We mathematically characterize the conditions under which our technique is impactful, including understanding the transfer learning nature of our set-up. We present three distinct experiments on synthetic data, time series data, and image domain, revealing the wide applicability of our technique.<\/jats:p>","DOI":"10.1609\/aaai.v36i6.20568","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T11:15:11Z","timestamp":1656933311000},"page":"6193-6201","source":"Crossref","is-referenced-by-count":0,"title":["Multiscale Generative Models: Improving Performance of a Generative Model Using Feedback from Other Dependent Generative Models"],"prefix":"10.1609","volume":"36","author":[{"given":"Changyu","family":"Chen","sequence":"first","affiliation":[]},{"given":"Avinandan","family":"Bose","sequence":"additional","affiliation":[]},{"given":"Shih-Fen","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Arunesh","family":"Sinha","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2022,6,28]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/20568\/20327","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/20568\/20327","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T11:15:11Z","timestamp":1656933311000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/20568"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,28]]},"references-count":0,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6,30]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v36i6.20568","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2022,6,28]]}}}