Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models
This work addresses the challenge for novices and experts in photo editing software by providing personalized and diverse edit suggestions, though it appears incremental as it builds on existing neural network generative modeling.
The paper tackled the problem of proposing multiple diverse, high-quality photo edits while adapting to user preferences, and found that their model outperformed other approaches on this multimodal prediction task.
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.