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公开(公告)号：US11934935B2
公开(公告)日：2024-03-19
申请号：US15985463
申请日：2018-05-21
Applicant: DeepMind Technologies Limited
Inventor： Aaron Gerard Antonius van den Oord , Karen Simonyan , Oriol Vinyals
Abstract: A feedforward generative neural network that generates an output example that includes multiple output samples of a particular type in a single neural network inference. Optionally, the generation may be conditioned on a context input. For example, the feedforward generative neural network may generate a speech waveform that is a verbalization of an input text segment conditioned on linguistic features of the text segment.
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公开(公告)号：US11636283B2
公开(公告)日：2023-04-25
申请号：US16889125
申请日：2020-06-01
Applicant: DeepMind Technologies Limited
Inventor： Benjamin Poole , Aaron Gerard Antonius van den Oord , Ali Razavi-Nematollahi , Oriol Vinyals
Abstract: A variational autoencoder (VAE) neural network system, comprising an encoder neural network to encode an input data item to define a posterior distribution for a set of latent variables, and a decoder neural network to generate an output data item representing values of a set of latent variables sampled from the posterior distribution. The system is configured for training with an objective function including a term dependent on a difference between the posterior distribution and a prior distribution. The prior and posterior distributions are arranged so that they cannot be matched to one another. The VAE system may be used for compressing and decompressing data.
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公开(公告)号：US11355097B2
公开(公告)日：2022-06-07
申请号：US17061437
申请日：2020-10-01
Applicant: DeepMind Technologies Limited
Inventor： Yutian Chen , Scott Ellison Reed , Aaron Gerard Antonius van den Oord , Oriol Vinyals , Heiga Zen , Ioannis Alexandros Assael , Brendan Shillingford , Joao Ferdinando Gomes de Freitas
IPC: G10L13/047 , G10L13/033 , G10L13/00 , G06N3/04 , G06N3/08
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an adaptive audio-generation model. One of the methods includes generating an adaptive audio-generation model including learning a plurality of embedding vectors and parameter values of a neural network using training data comprising first text and audio data representing a plurality of different individual speakers speaking portions of the first text, wherein the plurality of embedding vectors represent respective voice characteristics of the plurality of different individual speakers. The adaptive audio-generation model is adapted for a new individual speaker using adaptation data comprising second text and audio data representing the new individual speaker speaking portions of the second text, the new individual speaker being different from each of the plurality of individual speakers, wherein adapting the audio-generation model includes learning a new embedding vector for the new individual speaker.
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公开(公告)号：US20210342670A1
公开(公告)日：2021-11-04
申请号：US17375250
申请日：2021-07-14
Applicant: DeepMind Technologies Limited
Inventor： Aaron Gerard Antonius van den Oord , Sander Etienne Lea Dieleman , Nal Emmerich Kalchbrenner , Karen Simonyan , Oriol Vinyals , Lasse Espeholt
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.
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公开(公告)号：US11144782B2
公开(公告)日：2021-10-12
申请号：US16338338
申请日：2017-09-29
Applicant: DeepMind Technologies Limited
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.
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Patent Agency Ranking
公开(公告)号：US20210192298A1
公开(公告)日：2021-06-24
申请号：US17198096
申请日：2021-03-10
Applicant: DeepMind Technologies Limited
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.
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公开(公告)号：US10949717B2
公开(公告)日：2021-03-16
申请号：US16537423
申请日：2019-08-09
Applicant: DeepMind Technologies Limited
IPC: G06K9/66 , G06N3/04 , G06N3/08 , G06K9/46 , H04N19/52 , G06K9/62 , H04N19/50 , H04N19/18 , H04N19/17 , 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.
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公开(公告)号：US20210027425A1
公开(公告)日：2021-01-28
申请号：US16324061
申请日：2018-02-26
Applicant: DeepMind Technologies Limited
Inventor： Nal Emmerich Kalchbrenner , Daniel Belov , Sergio Gomez Colmenarejo , Aaron Gerard Antonius van den Oord , Ziyu Wang , Joao Gomes de Freitas , Scott Ellison Reed
Abstract: A method of generating an output image having an output resolution of N pixels×N pixels, each pixel in the output image having a respective color value for each of a plurality of color channels, the method comprising: obtaining a low-resolution version of the output image; and upscaling the low-resolution version of the output image to generate the output image having the output resolution by repeatedly performing the following operations: obtaining a current version of the output image having a current K×K resolution; and processing the current version of the output image using a set of convolutional neural networks that are specific to the current resolution to generate an updated version of the output image having a 2K×2K resolution.
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公开(公告)号：US10671889B2
公开(公告)日：2020-06-02
申请号：US16586014
申请日：2019-09-27
Applicant: DeepMind Technologies Limited
Inventor： Benjamin Poole , Aaron Gerard Antonius van den Oord , Ali Razavi-Nematollahi , Oriol Vinyals
Abstract: A variational autoencoder (VAE) neural network system, comprising an encoder neural network to encode an input data item to define a posterior distribution for a set of latent variables, and a decoder neural network to generate an output data item representing values of a set of latent variables sampled from the posterior distribution. The system is configured for training with an objective function including a term dependent on a difference between the posterior distribution and a prior distribution. The prior and posterior distributions are arranged so that they cannot be matched to one another. The VAE system may be used for compressing and decompressing data.
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公开(公告)号：US20200104640A1
公开(公告)日：2020-04-02
申请号：US16586014
申请日：2019-09-27
Applicant: DeepMind Technologies Limited
Inventor： Benjamin Poole , Aaron Gerard Antonius van den Oord , Ali Razavi-Nematollahi , Oriol Vinyals
Abstract: A variational autoencoder (VAE) neural network system, comprising an encoder neural network to encode an input data item to define a posterior distribution for a set of latent variables, and a decoder neural network to generate an output data item representing values of a set of latent variables sampled from the posterior distribution. The system is configured for training with an objective function including a term dependent on a difference between the posterior distribution and a prior distribution. The prior and posterior distributions are arranged so that they cannot be matched to one another. The VAE system may be used for compressing and decompressing data.
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