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Learning Implicit Generative Models with the Method of Learned Moments
Suman Ravuri · Shakir Mohamed · Mihaela Rosca · Oriol Vinyals

Wed Jul 11 09:15 AM -- 12:00 PM (PDT) @ Hall B #112

We propose a method of moments (MoM) algorithm for training large-scale implicit generative models. Moment estimation in this setting encounters two problems: it is often difficult to define the millions of moments needed to learn the model parameters, and it is hard to determine which properties are useful when specifying moments. To address the first issue, we introduce a moment network, and define the moments as the network's hidden units and the gradient of the network's output with respect to its parameters. To tackle the second problem, we use asymptotic theory to highlight desiderata for moments -- namely they should minimize the asymptotic variance of estimated model parameters -- and introduce an objective to learn better moments. The sequence of objectives created by this Method of Learned Moments (MoLM) can train high-quality neural image samplers. On CIFAR-10, we demonstrate that MoLM-trained generators achieve significantly higher Inception Scores and lower Frechet Inception Distances than those trained with gradient penalty-regularized and spectrally-normalized adversarial objectives. These generators also achieve nearly perfect Multi-Scale Structural Similarity Scores on CelebA, and can create high-quality samples of 128x128 images.

Author Information

Suman Ravuri (DeepMind)
Shakir Mohamed (DeepMind)

Shakir Mohamed works on technical and sociotechnical questions in machine learning research, working on problems in machine learning principles, applied problems in healthcare and environment, and ethics and diversity. Shakir is a Director for Research at DeepMind in London, an Associate Fellow at the Leverhulme Centre for the Future of Intelligence, and an Honorary Professor of University College London. Shakir is also a founder and trustee of the Deep Learning Indaba, a grassroots charity whose work is to build pan-African capacity and leadership in AI. Amongst other roles, Shakir served as the senior programme chair for ICLR 2021, and as the General Chair for NeurIPS 2022. Shakir also serves on the Board of Directors for some of the leading conferences in the field of machine learning and AI (ICML, ICLR, NeurIPS), is a member of the Royal Society diversity and inclusion committee, and on the international scientific advisory committee for the pan-Canadian AI strategy. Shakir is from South Africa, completed a postdoc at the University of British Columbia, received his PhD from the University of Cambridge, and received his masters and undergraduate degrees in Electrical and Information engineering from the University of the Witwatersrand, Johannesburg.

Mihaela Rosca (DeepMind)
Oriol Vinyals (DeepMind)

Oriol Vinyals is a Research Scientist at Google. He works in deep learning with the Google Brain team. Oriol holds a Ph.D. in EECS from University of California, Berkeley, and a Masters degree from University of California, San Diego. He is a recipient of the 2011 Microsoft Research PhD Fellowship. He was an early adopter of the new deep learning wave at Berkeley, and in his thesis he focused on non-convex optimization and recurrent neural networks. At Google Brain he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, language, and vision.

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