公开(公告)号：US10402700B2

公开(公告)日：2019-09-03

申请号：US15721089

申请日：2017-09-29

Applicant: DeepMind Technologies Limited

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

IPC: G06K9/66 ,  H04N19/50 ,  H04N19/52 ,  G06N3/04 ,  G06N3/08 ,  G06K9/46 ,  G06K9/62 ,  H04N19/186 ,  H04N19/172 ,  H04N19/182

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：US20240135955A1

公开(公告)日：2024-04-25

申请号：US18519986

申请日：2023-11-27

Applicant: DeepMind Technologies Limited

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

IPC: G10L25/30 ,  G06N3/04 ,  G06N3/045 ,  G06N3/048 ,  G10L13/06

CPC classification number: G10L25/30 ,  G06N3/04 ,  G06N3/045 ,  G06N3/048 ,  G10L13/06 ,  G10H2250/311

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output sequence of audio data that comprises a respective audio sample at each of a plurality of time steps. 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：US11869530B2

公开(公告)日：2024-01-09

申请号：US17838985

申请日：2022-06-13

Applicant: DeepMind Technologies Limited

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

IPC: G10L25/30 ,  G10L13/06 ,  G06N3/045 ,  G06N3/048 ,  G06N3/04

CPC classification number: G10L25/30 ,  G06N3/04 ,  G06N3/045 ,  G06N3/048 ,  G10L13/06 ,  G10H2250/311

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output sequence of audio data that comprises a respective audio sample at each of a plurality of time steps. 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：US11468295B2

公开(公告)日：2022-10-11

申请号：US15985628

申请日：2018-05-21

Applicant: DeepMind Technologies Limited

Inventor： Nal Emmerich Kalchbrenner ,  Karen Simonyan ,  Erich Konrad Elsen

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output examples using neural networks. One of the methods includes receiving a request to generate an output example of a particular type, accessing dependency data, and generating the output example by, at each of a plurality of generation time steps: identifying one or more current blocks for the generation time step, wherein each current block is a block for which the values of the bits in all of the other blocks identified in the dependency for the block have already been generated; and generating the values of the bits in the current blocks for the generation time step conditioned on, for each current block, the already generated values of the bits in the other blocks identified in the dependency for the current block.

公开(公告)号：US20220319533A1

公开(公告)日：2022-10-06

申请号：US17838985

申请日：2022-06-13

Applicant: DeepMind Technologies Limited

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

IPC: G10L25/30 ,  G06N3/04 ,  G10L13/06

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output sequence of audio data that comprises a respective audio sample at each of a plurality of time steps. 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：US11386914B2

公开(公告)日：2022-07-12

申请号：US17020348

申请日：2020-09-14

Applicant: DeepMind Technologies Limited

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

IPC: G10L25/30 ,  G06N3/04 ,  G10L13/06

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output sequence of audio data that comprises a respective audio sample at each of a plurality of time steps. 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：US11348203B2

公开(公告)日：2022-05-31

申请号：US16927490

申请日：2020-07-13

Applicant: DeepMind Technologies Limited

Inventor： Nal Emmerich Kalchbrenner ,  Jacob Lee Menick

IPC: G06T3/40

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output images. One of the methods includes obtaining data specifying (i) a partitioning of the H by W pixel grid of the output image into K disjoint, interleaved sub-images and (ii) an ordering of the sub-images; and generating intensity values sub-image by sub-image, comprising: for each particular color channel for each particular pixel in each particular sub-image, generating, using a generative neural network, the intensity value for the particular color channel conditioned on intensity values for (i) any pixels that are in sub-images that are before the particular sub-image in the ordering, (ii) any pixels within the particular sub-image that are before the particular pixel in a raster-scan order over the output image, and (iii) the particular pixel for any color channels that are before the particular color channel in a color channel order.

公开(公告)号：US20210019555A1

公开(公告)日：2021-01-21

申请号：US16338338

申请日：2017-09-29

Applicant: DeepMind Technologies Limited

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

IPC: G06K9/62 ,  G06K9/46 ,  G06K9/00 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating video frames using neural networks. One of the methods includes processing a sequence of video frames using an encoder neural network to generate an encoded representation; and generating a predicted next frame pixel by pixel according to a pixel order and a channel order, comprising: for each color channel of each pixel, providing as input to a decoder neural network (i) the encoded representation, (ii) color values for any pixels before the pixel in the pixel order, and (iii) color values for the pixel for any color channels before the color channel in the channel order, wherein the decoder neural network is configured to generate an output defining a score distribution over a plurality of possible color values, and determining the color value for the color channel of the pixel by sampling from the score distribution.

公开(公告)号：US10482373B1

公开(公告)日：2019-11-19

申请号：US15174806

申请日：2016-06-06

Applicant: DeepMind Technologies Limited

Inventor： Nal Emmerich Kalchbrenner ,  Ivo Danihelka ,  Alexander Benjamin Graves

IPC: G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing grid Long Short-Term Memory (LSTM) neural networks that includes a plurality of N-LSTM blocks arranged in an N-dimensional grid. Each N-LSTM block is configured to: receive N input hidden vectors, the N input hidden vectors each corresponding to a respective one of the N dimensions; receive N input memory vectors, the N input memory vectors each corresponding to a respective one of the N dimensions; and, for each of the dimensions, apply a respective transform for the dimension to the memory hidden vector corresponding to the dimension and the input hidden vector corresponding to the dimension to generate a new hidden vector corresponding to the dimension and a new memory vector corresponding to the dimension.

公开(公告)号：US20180336455A1

公开(公告)日：2018-11-22

申请号：US15985628

申请日：2018-05-21

Applicant: DeepMind Technologies Limited

Inventor： Nal Emmerich Kalchbrenner ,  Karen Simonyan ,  Erich Konrad Elsen

IPC: G06N3/04

CPC classification number: G06N3/088 ,  G06N3/0454

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating output examples using neural networks. One of the methods includes receiving a request to generate an output example of a particular type, accessing dependency data, and generating the output example by, at each of a plurality of generation time steps: identifying one or more current blocks for the generation time step, wherein each current block is a block for which the values of the bits in all of the other blocks identified in the dependency for the block have already been generated; and generating the values of the bits in the current blocks for the generation time step conditioned on, for each current block, the already generated values of the bits in the other blocks identified in the dependency for the current block.