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Learning via Social Awareness: Improving a Deep Generative Sketching Model with Facial Feedback

Natasha Jaques
Jennifer McCleary
David Ha
Fred Bertsch
Rosalind Picard
ICLR 2018 Workshop

Abstract

In the quest towards general artificial intelligence (AI), researchers have explored developing loss functions that function as intrinsic motivators in the absence of external rewards. This paper takes the position that current research has overlooked an important and useful intrinsic motivator: social interaction. We posit that making an AI agent aware of implicit social feedback from humans can allow for more rapid learning of more generalizable and useful representations, and could potentially impact AI safety. We collect social feedback in the form of facial expression reactions to samples from Sketch RNN, an LSTM-based variational autoencoder designed to produce sketch drawings. We use a Latent Constraints GAN (LC-GAN) to learn from the facial feedback of a small group of viewers, and then show in an independent evaluation with 76 users that this model produced sketches that lead to significantly more smiling and less frowning than the baseline. Thus, we establish that implicit social feedback can improve the output of a deep learning model.