Multimodal prediction and personalization of photo edits with deep generative models

Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams

Research output: Contribution to conferencePaper

2 Scopus citations

Abstract

Professional-grade software applications are powerful but complicated—expert users can achieve impressive results, but novices often struggle to complete even basic tasks. Photo editing is a prime example: after loading a photo, the user is confronted with an array of cryptic sliders like “clarity”, “temp”, and “highlights”. An automatically generated suggestion could help, but there is no single “correct” edit for a given image—different experts may make very different aesthetic decisions when faced with the same image, and a single expert may make different choices depending on the intended use of the image (or on a whim). We therefore want a system that can propose multiple diverse, high-quality edits while also learning from and adapting to a user’s aesthetic preferences. In this work, we develop a statistical model that meets these objectives. Our model builds on recent advances in neural network generative modeling and scalable inference, and uses hierarchical structure to learn editing patterns across many diverse users. Empirically, we find that our model outperforms other approaches on this challenging multimodal prediction task.

Original languageEnglish (US)
Pages1309-1317
Number of pages9
StatePublished - Jan 1 2018
Externally publishedYes
Event21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 - Playa Blanca, Lanzarote, Canary Islands, Spain
Duration: Apr 9 2018Apr 11 2018

Conference

Conference21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018
CountrySpain
CityPlaya Blanca, Lanzarote, Canary Islands
Period4/9/184/11/18

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Artificial Intelligence

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    Saeedi, A., Hoffman, M. D., DiVerdi, S. J., Ghandeharioun, A., Johnson, M. J., & Adams, R. P. (2018). Multimodal prediction and personalization of photo edits with deep generative models. 1309-1317. Paper presented at 21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018, Playa Blanca, Lanzarote, Canary Islands, Spain.