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公开(公告)号：US11657479B2
公开(公告)日：2023-05-23
申请号：US17445362
申请日：2021-08-18
Applicant: Snap Inc.
Inventor： Sergey Demyanov , Aleksei Podkin , Aleksei Stoliar , Vadim Velicodnii , Fedor Zhdanov
CPC classification number: G06T5/001 , G06T5/10 , G06T2200/16 , G06T2207/10004 , G06T2207/20048 , G06T2207/20081 , G06T2207/30196 , H04L51/10
Abstract: A mobile device can implement a neural network-based domain transfer scheme to modify an image in a first domain appearance to a second domain appearance. The domain transfer scheme can be configured to detect an object in the image, apply an effect to the image, and blend the image using color space adjustments and blending schemes to generate a realistic result image. The domain transfer scheme can further be configured to efficiently execute on the constrained device by removing operational layers based on resources available on the mobile device.
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公开(公告)号：US11580400B1
公开(公告)日：2023-02-14
申请号：US16586635
申请日：2019-09-27
Applicant: Snap Inc.
Inventor： Enxu Yan , Sergey Tulyakov , Aleksei Podkin , Aleksei Stoliar
Abstract: A neural network pruning system can sparsely prune neural network models using an optimizer based approach that is agnostic to the model architecture being pruned. The neural network pruning system can prune by operating on the parameter vector of the full model and the gradient vector of the loss function with respect to the model parameters. The neural network pruning system can iteratively update parameters based on the gradients, while zeroing out as many parameters as possible based a preconfigured penalty.
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公开(公告)号：US20210383509A1
公开(公告)日：2021-12-09
申请号：US17445362
申请日：2021-08-18
Applicant: Snap Inc.
Inventor： Sergey Demyanov , Aleksei Podkin , Aleksei Stoliar , Vadim Velicodnii , Fedor Zhdanov
Abstract: A mobile device can implement a neural network-based domain transfer scheme to modify an image in a first domain appearance to a second domain appearance. The domain transfer scheme can be configured to detect an object in the image, apply an effect to the image, and blend the image using color space adjustments and blending schemes to generate a realistic result image. The domain transfer scheme can further be configured to efficiently execute on the constrained device by removing operational layers based on resources available on the mobile device.
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公开(公告)号：US20210299630A1
公开(公告)日：2021-09-30
申请号：US17330852
申请日：2021-05-26
Applicant: Snap Inc.
Inventor： Grygoriy Kozhemiak , Oleksandr Pyshchenko , Victor Shaburov , Trevor Stephenson , Aleksei Stoliar
Abstract: Systems and methods are provided for receiving a first media content item associated with a first interactive object of an interactive message, receiving a second media content item associated with a second interactive object of the interactive message, generating a third media content item based on the first media content item and second media content item, wherein the third media content item comprises combined features of the first media content item and the second media content item, and causing display of the generated third media content item.
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公开(公告)号：US11120526B1
公开(公告)日：2021-09-14
申请号：US16376564
申请日：2019-04-05
Applicant: Snap Inc.
Inventor： Sergey Demyanov , Aleksei Podkin , Aleksei Stoliar , Vadim Velicodnii , Fedor Zhdanov
Abstract: A mobile device can implement a neural network-based domain transfer scheme to modify an image in a first domain appearance to a second domain appearance. The domain transfer scheme can be configured to detect an object in the image, apply an effect to the image, and blend the image using color space adjustments and blending schemes to generate a realistic result image. The domain transfer scheme can further be configured to efficiently execute on the constrained device by removing operational layers based on resources available on the mobile device.
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Patent Agency Ranking
公开(公告)号：US20210192198A1
公开(公告)日：2021-06-24
申请号：US17138177
申请日：2020-12-30
Applicant: Snap Inc.
Inventor： Sergey Tulyakov , Roman Furko , Aleksei Stoliar
Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.
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公开(公告)号：US20250054201A1
公开(公告)日：2025-02-13
申请号：US18231886
申请日：2023-08-09
Applicant: Snap Inc.
Inventor： Ekaterina Deyneka , Andrey Alejandrovich Gomez Zharkov , Sergey Tulyakov , Aleksei Stoliar , Konstantin Gudkov
IPC: G06T11/00 , G06V10/774 , G06V10/776
Abstract: Methods and systems are disclosed for enhancing or modifying an image by a machine learning model. The methods and systems receive an image depicting a real-world object. The methods and systems analyze the image using a machine learning model to generate a modified image that depicts one or more augmented reality stylizations overlaid on the real-world object, the machine learning model trained in multiple stages having different training data sets and different conditions applied in each of the multiple stages. The methods and systems present the modified image on a device.
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公开(公告)号：US12165244B2
公开(公告)日：2024-12-10
申请号：US17974400
申请日：2022-10-26
Applicant: Snap Inc.
Inventor： Olha Rykhliuk , Jonathan Solichin , Aleksei Stoliar
Abstract: Systems and methods herein describe receiving an image via an image capture device, using a machine learning model, generating an image augmentation decision, accessing an augmented reality content item, associating the generated image augmentation decision with the augmented reality content item, modifying the received image using the augmented reality content item and the associated image augmentation decision, and causing presentation of the modified image on a graphical user interface of a computing device.
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公开(公告)号：US11900565B2
公开(公告)日：2024-02-13
申请号：US18116682
申请日：2023-03-02
Applicant: Snap Inc.
Inventor： Sergey Demyanov , Aleksei Podkin , Aleksei Stoliar , Vadim Velicodnii , Fedor Zhdanov
CPC classification number: G06T5/001 , G06T5/10 , G06T2200/16 , G06T2207/10004 , G06T2207/20048 , G06T2207/20081 , G06T2207/30196 , H04L51/10
Abstract: A data item is identified on a device. A neural network that includes an adversarial transformation subnetwork is applied to the data item to generate a modified data item. Output indicative of the modified data item is caused to be presented on the device. The neural network further comprises an encoder and a decoder. The neural network is trained in at least two stages. At least one of the encoder and the decoder is trained in a first stage and the adversarial transformation subnetwork is trained in a second stage.
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公开(公告)号：US20230334327A1
公开(公告)日：2023-10-19
申请号：US18213145
申请日：2023-06-22
Applicant: Snap Inc.
Inventor： Sergey Tulyakov , Sergei Korolev , Aleksei Stoliar , Maksim Gusarov , Sergei Kotcur , Christopher Yale Crutchfield , Andrew Wan
IPC: G06N3/088 , G06N3/08 , G06F18/21 , G06F18/214 , G06N3/045 , G06V10/764 , G06V10/774 , G06V10/778 , G06V10/82
CPC classification number: G06N3/088 , G06N3/08 , G06F18/2185 , G06F18/2148 , G06N3/045 , G06V10/764 , G06V10/7747 , G06V10/7788 , G06V10/82
Abstract: A compact generative neural network can be distilled from a teacher generative neural network using a training network. The compact network can be trained on the input data and output data of the teacher network. The training network train the student network using a discrimination layer and one or more types of losses, such as perception loss and adversarial loss.
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