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High-Performance Large-Scale Image Recognition Without Normalization
Andrew Brock, Soham De, Samuel L. Smith +1
Batch normalization is a key component of most image classification models, but it has many undesirable properties stemming from its dependence on the batch size and interactions b…
Fast Sparse ConvNets
Erich Elsen, Marat Dukhan, Trevor Gale +1
Historically, the pursuit of efficient inference has been one of the driving forces behind research into new deep learning architectures and building blocks. Some recent examples i…
Adversarial Video Generation on Complex Datasets
Aidan Clark, Jeff Donahue, Karen Simonyan
Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale. We attempt to carry this success to the field of video modeling…
Large Scale Adversarial Representation Learning
Jeff Donahue, Karen Simonyan
Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation…
Hierarchical Autoregressive Image Models with Auxiliary Decoders
Jeffrey De Fauw, Sander Dieleman, Karen Simonyan
Autoregressive generative models of images tend to be biased towards capturing local structure, and as a result they often produce samples which are lacking in terms of large-scale…
The Kinetics Human Action Video Dataset
Will Kay, Joao Carreira, Karen Simonyan +9
We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 1…