公开(公告)号：US20230368337A1

公开(公告)日：2023-11-16

申请号：US18182271

申请日：2023-03-10

Applicant: NVIDIA CORPORATION

Inventor： Tero Tapani KARRAS ,  Miika AITTALA ,  Timo Oskari AILA ,  Samuli LAINE

IPC: G06T5/00

CPC classification number: G06T5/002 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Techniques are disclosed herein for generating a content item. The techniques include receiving a content item and metadata indicating a level of corruption associated with the content item; and for each iteration included in a plurality of iterations: performing one or more operations to add corruption to a first version of the content item to generate a second version of the content item, and performing one or more operations to reduce corruption in the second version of the content item to generate a third version of the content item, wherein a level of corruption associated with the third version of the content item is less than a level of corruption associated with the first version of the content item.

公开(公告)号：US20240161250A1

公开(公告)日：2024-05-16

申请号：US18485239

申请日：2023-10-11

Applicant: NVIDIA CORPORATION

Inventor： Yogesh BALAJI ,  Timo Oskari AILA ,  Miika AITTALA ,  Bryan CATANZARO ,  Xun HUANG ,  Tero Tapani KARRAS ,  Karsten KREIS ,  Samuli LAINE ,  Ming-Yu LIU ,  Seungjun NAH ,  Jiaming SONG ,  Arash VAHDAT ,  Qinsheng ZHANG

IPC: G06T5/00

CPC classification number: G06T5/002 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Techniques are disclosed herein for generating a content item. The techniques include performing one or more first denoising operations based on an input and a first machine learning model to generate a first content item, and performing one or more second denoising operations based on the input, the first content item, and a second machine learning model to generate a second content item, where the first machine learning model is trained to denoise content items having an amount of corruption within a first corruption range, the second machine learning model is trained to denoise content items having an amount of corruption within a second corruption range, and the second corruption range is lower than the first corruption range.

公开(公告)号：US20230368073A1

公开(公告)日：2023-11-16

申请号：US18182283

申请日：2023-03-10

Applicant: NVIDIA CORPORATION

Inventor： Tero Tapani KARRAS ,  Miika AITTALA ,  Timo Oskari AILA ,  Samuli LAINE

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Techniques are disclosed herein for generating a content item. The techniques include receiving a content item and metadata indicating a level of corruption associated with the content item; and for each iteration included in a plurality of iterations: performing one or more operations to add corruption to a first version of the content item to generate a second version of the content item, and performing one or more operations to reduce corruption in the second version of the content item to generate a third version of the content item, wherein a level of corruption associated with the third version of the content item is less than a level of corruption associated with the first version of the content item.

公开(公告)号：US20240303494A1

公开(公告)日：2024-09-12

申请号：US18666613

申请日：2024-05-16

Applicant: NVIDIA Corporation

Inventor： Ming-Yu LIU ,  Xun HUANG ,  Tero Tapani KARRAS ,  Timo AILA ,  Jaakko LEHTINEN

IPC: G06N3/088 ,  G06F18/214 ,  G06F18/2431 ,  G06T3/02 ,  G06T3/60 ,  G06T7/73 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06N3/088 ,  G06F18/214 ,  G06F18/2431 ,  G06T3/02 ,  G06T3/60 ,  G06T7/74 ,  G06V10/764 ,  G06V10/82 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A few-shot, unsupervised image-to-image translation (“FUNIT”) algorithm is disclosed that accepts as input images of previously-unseen target classes. These target classes are specified at inference time by only a few images, such as a single image or a pair of images, of an object of the target type. A FUNIT network can be trained using a data set containing images of many different object classes, in order to translate images from one class to another class by leveraging few input images of the target class. By learning to extract appearance patterns from the few input images for the translation task, the network learns a generalizable appearance pattern extractor that can be applied to images of unseen classes at translation time for a few-shot image-to-image translation task.

公开(公告)号：US20140123150A1

公开(公告)日：2014-05-01

申请号：US13660741

申请日：2012-10-25

Applicant: NVIDIA CORPORATION

Inventor： John Erik LINDHOLM ,  Tero Tapani KARRAS ,  Samuli Matias LAINE ,  Timo AILA

IPC: G06F9/46

CPC classification number: G06F9/522 ,  G06F9/30087 ,  G06F9/3834 ,  G06F9/3851 ,  G06F9/4881 ,  G06F2209/484

Abstract: One embodiment sets forth a technique for scheduling the execution of ordered critical code sections by multiple threads. A multithreaded processor includes an instruction scheduling unit that is configured to schedule threads to process ordered critical code sections. A ordered critical code section is preceded by a barrier instruction and when all of the threads have reached the barrier instruction, the instruction scheduling unit controls the thread execution order by selecting each thread for execution based on logical identifiers associated with the threads. The logical identifiers are mapped to physical identifiers that are referenced by the multithreaded processor during execution of the threads. The logical identifiers are used by the instruction scheduling unit to control the order in which the threads execute the ordered critical code section.

Abstract translation: 一个实施例提出了一种用于通过多个线程来调度有序关键代码段的执行的技术。 多线程处理器包括指令调度单元，其被配置为调度线程以处理有序的关键代码段。 有序的关键代码部分之前是屏障指令，并且当所有线程已经到达屏障指令时，指令调度单元通过基于与线程相关联的逻辑标识符选择用于执行的每个线程来控制线程执行顺序。 逻辑标识符被映射到在执行线程期间由多线程处理器引用的物理标识符。 逻辑标识符被指令调度单元用于控制线程执行有序关键代码段的顺序。