OpenAI Blog · Mar 21, 2019
Implicit generation and generalization methods for energy-based models
Reviewed by Errol Vogt, Site support technician & online learning analyst · original summary · editorial policy
Implicit generation and generalization methods for energy-based models. We’ve made progress towards stable and scalable training of energy-based models (EBMs) resulting in better sample quality and generalization ability than existing models. Generation in EBMs spends more compute to continually refine its answers and doing so can generate samples competitive with GANs at low temperatures, while also having mode coverage guarantees of likelihood-based models. We hope these findings stimulate further research into this promising class of models. This update is relevant for small-office operators tracking changes in their tools.
Operator takeaway: For operators: review whether 'Implicit generation and generalization methods for energy-based models' affects your current setup before relying on it in production.
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