Accurate and Diverse Sampling of Sequences Based on a “Best of Many” Sample Objective

Apratim Bhattacharyya, Bernt Schiele, Mario Fritz; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 8485-8493

Abstract


For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence. This problem has been formalized as a sequence extrapolation problem, where a number of observations are used to predict the sequence into the future. Real-world scenarios demand a model of uncertainty of such predictions, as predictions become increasingly uncertain -- in particular on long time horizons. While impressive results have been shown on point estimates, scenarios that induce multi-modal distributions over future sequences remain challenging. Our work addresses these challenges in a Gaussian Latent Variable model for sequence prediction. Our core contribution is a ``Best of Many'' sample objective that leads to more accurate and more diverse predictions that better capture the true variations in real-world sequence data. Beyond our analysis of improved model fit, our models also empirically outperform prior work on three diverse tasks ranging from traffic scenes to weather data.

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[bibtex]
@InProceedings{Bhattacharyya_2018_CVPR,
author = {Bhattacharyya, Apratim and Schiele, Bernt and Fritz, Mario},
title = {Accurate and Diverse Sampling of Sequences Based on a “Best of Many” Sample Objective},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}