Energy based models (EBMs) are appealing due to their generality and simplicity in likelihood modeling, but have been traditionally difficult to train. We present techniques to scale MCMC based EBM training on continuous neural networks, and we show its success on the high-dimensional data domains of ImageNet32x32, ImageNet128x128, CIFAR-10, and robotic hand trajectories, achieving better samples than other likelihood models and nearing the performance of contemporary GAN approaches, while covering all modes of the data. We highlight some unique capabilities of implicit generation such as compositionality and corrupt image reconstruction and inpainting. Finally, we show that EBMs are useful models across a wide variety of tasks, achieving state-of-the-art out-of-distribution classification, adversarially robust classification, state-of-the-art continual online class learning, and coherent long term predicted trajectory rollouts.
Author Information
Yilun Du (MIT)
Igor Mordatch (OpenAI)
Related Events (a corresponding poster, oral, or spotlight)
-
2019 Poster: Implicit Generation and Modeling with Energy Based Models »
Tue Dec 10th 05:30 -- 07:30 PM Room East Exhibition Hall B + C
More from the Same Authors
-
2018 Workshop: Emergent Communication Workshop »
Jakob Foerster · Angeliki Lazaridou · Ryan Lowe · Igor Mordatch · Douwe Kiela · Kyunghyun Cho -
2018 Poster: Learning to Exploit Stability for 3D Scene Parsing »
Yilun Du · Zhijian Liu · Hector Basevi · Ales Leonardis · Bill Freeman · Josh Tenenbaum · Jiajun Wu -
2017 Workshop: Emergent Communication Workshop »
Jakob Foerster · Igor Mordatch · Angeliki Lazaridou · Kyunghyun Cho · Douwe Kiela · Pieter Abbeel -
2017 Poster: Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments »
Ryan Lowe · YI WU · Aviv Tamar · Jean Harb · OpenAI Pieter Abbeel · Igor Mordatch
