公开(公告)号：US20160358073A1

公开(公告)日：2016-12-08

申请号：US15174020

申请日：2016-06-06

Applicant: Google Inc.

Inventor： Guillaume Desjardins ,  Karen Simonyan ,  Koray Kavukcuoglu ,  Razvan Pascanu

IPC: G06N3/08 ,  G06N99/00 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/084 ,  G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using a neural network system that includes a whitened neural network layer. One of the methods includes receiving an input activation generated by a layer before the whitened neural network layer in the sequence; processing the received activation in accordance with a set of whitening parameters to generate a whitened activation; processing the whitened activation in accordance with a set of layer parameters to generate an output activation; and providing the output activation as input to a neural network layer after the whitened neural network layer in the sequence.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于使用包含白化神经网络层的神经网络系统处理输入。 其中一种方法包括在序列中接收由白化神经网络层之前的层产生的输入激活; 根据一组增白参数来处理接收到的激活以产生白化激活; 根据一组层参数处理白化激活以产生输出激活; 并且在序列中的白化神经网络层之后，将输出激活提供给神经网络层的输入。

公开(公告)号：US10068557B1

公开(公告)日：2018-09-04

申请号：US15684537

申请日：2017-08-23

Applicant: Google Inc.

Inventor： Jesse Engel ,  Mohammad Norouzi ,  Karen Simonyan ,  Adam Roberts ,  Cinjon Resnick ,  Sander Etienne Lea Dieleman ,  Douglas Eck

IPC: G10H1/00 ,  G06N3/08 ,  G06N3/04 ,  G10H7/12

Abstract: The present disclosure provides systems and methods that include or otherwise leverage a machine-learned neural synthesizer model. Unlike a traditional synthesizer which generates audio from hand-designed components like oscillators and wavetables, the neural synthesizer model can use deep neural networks to generate sounds at the level of individual samples. Learning directly from data, the neural synthesizer model can provide intuitive control over timbre and dynamics and enable exploration of new sounds that would be difficult or impossible to produce with a hand-tuned synthesizer. As one example, the neural synthesizer model can be a neural synthesis autoencoder that includes an encoder model that learns embeddings descriptive of musical characteristics and an autoregressive decoder model that is conditioned on the embedding to autoregressively generate musical waveforms that have the musical characteristics one audio sample at a time.

公开(公告)号：US20180025257A1

公开(公告)日：2018-01-25

申请号：US15721089

申请日：2017-09-29

Applicant: Google Inc.

Inventor： Aaron Gerard Antonius van den Oord ,  Nal Emmerich Kalchbrenner ,  Karen Simonyan

IPC: G06K9/66 ,  H04N19/52 ,  G06K9/46 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06K9/66 ,  G06K9/4652 ,  G06K9/623 ,  G06N3/04 ,  G06N3/0445 ,  G06N3/0454 ,  G06N3/08 ,  G06N3/084 ,  H04N19/172 ,  H04N19/182 ,  H04N19/186 ,  H04N19/50 ,  H04N19/52

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating images using neural networks. One of the methods includes generating the output image pixel by pixel from a sequence of pixels taken from the output image, comprising, for each pixel in the output image, generating a respective score distribution over a discrete set of possible color values for each of the plurality of color channels.

公开(公告)号：US20180075343A1

公开(公告)日：2018-03-15

申请号：US15697407

申请日：2017-09-06

Applicant: Google Inc.

Inventor： Aaron Gerard Antonius van den Oord ,  Sander Etienne Lea Dieleman ,  Nal Emmerich Kalchbrenner ,  Karen Simonyan ,  Oriol Vinyals ,  Lasse Espeholt

IPC: G06N3/04 ,  G06F17/27 ,  G10L25/30 ,  G10L15/16 ,  G10L13/08

CPC classification number: G06N3/0472 ,  G06F17/18 ,  G06F17/2765 ,  G06F17/2818 ,  G06N3/0445 ,  G06N3/0454 ,  G06N3/084 ,  G10H2250/311 ,  G10L13/04 ,  G10L13/086 ,  G10L15/16 ,  G10L25/30

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing sequences using convolutional neural networks. One of the methods includes, for each of the time steps: providing a current sequence of audio data as input to a convolutional subnetwork, wherein the current sequence comprises the respective audio sample at each time step that precedes the time step in the output sequence, and wherein the convolutional subnetwork is configured to process the current sequence of audio data to generate an alternative representation for the time step; and providing the alternative representation for the time step as input to an output layer, wherein the output layer is configured to: process the alternative representation to generate an output that defines a score distribution over a plurality of possible audio samples for the time step.

公开(公告)号：US20160358038A1

公开(公告)日：2016-12-08

申请号：US15174133

申请日：2016-06-06

Applicant: Google Inc.

Inventor： Maxwell Elliot Jaderberg ,  Karen Simonyan ,  Andrew Zisserman ,  Koray Kavukcuoglu

IPC: G06K9/52 ,  G06N3/08 ,  G06K9/03

CPC classification number: G06K9/527 ,  G06K9/03 ,  G06K9/4628 ,  G06N3/0454 ,  G06N3/084 ,  G06N3/088

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于使用包括空间变换器模块的图像处理神经网络系统处理输入。 其中一种方法包括接收从一个或多个输入图像导出的输入特征图，以及将空间变换应用于输入特征图以产生变换后的特征图，包括：处理输入特征图以产生空间变换参数 空间变换，并根据空间变换参数从输入特征图进行采样，生成变换后的特征图。