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Poster
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
Mehmet Ozgur Turkoglu · Alexander Becker · Hüseyin Anil Gündüz · Mina Rezaei · Bernd Bischl · Rodrigo Caye Daudt · Stefano D'Aronco · Jan Wegner · Konrad Schindler

Wed Nov 30 02:00 PM -- 04:00 PM (PST) @ Hall J #506

The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncertainty predictions. A common approach to quantify epistemic uncertainty, usable across a wide class of prediction models, is to train a model ensemble. In a naive implementation, the ensemble approach has high computational cost and high memory demand. This challenges in particular modern deep learning, where even a single deep network is already demanding in terms of compute and memory, and has given rise to a number of attempts to emulate the model ensemble without actually instantiating separate ensemble members. We introduce FiLM-Ensemble, a deep, implicit ensemble method based on the concept of Feature-wise Linear Modulation (FiLM). That technique was originally developed for multi-task learning, with the aim of decoupling different tasks. We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison. Empirically, FiLM-Ensemble outperforms other implicit ensemble methods, and it comes very close to the upper bound of an explicit ensemble of networks (sometimes even beating it), at a fraction of the memory cost.

Author Information

Mehmet Ozgur Turkoglu (ETH Zurich)
Alexander Becker (ETH Zurich)
Hüseyin Anil Gündüz (LMU Munich)
Mina Rezaei (Ludwig-Maximilian University)
Bernd Bischl (LMU)
Rodrigo Caye Daudt (ETH Zurich)
Stefano D'Aronco (Swiss Federal Institute of Technology)
Jan Wegner (University of Zurich)
Konrad Schindler (ETH Zürich)

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