公开(公告)号：US20220327358A1

公开(公告)日：2022-10-13

申请号：US17808274

申请日：2022-06-22

Applicant: Snap Inc.

Inventor： Jacob Minyoung Huh ,  Shao-Hua Sun ,  Ning Zhang

IPC: G06N3/04 ,  G06V30/194 ,  G06N3/08

Abstract: Disclosed is a feedback adversarial learning framework, a recurrent framework for generative adversarial networks that can be widely adapted to not only stabilize training but also generate higher quality images. In some aspects, a discriminator's spatial outputs are distilled to improve generation quality. The disclosed embodiments model the discriminator into the generator, and the generator learns from its mistakes over time. In some aspects, a discriminator architecture encourages the model to be locally and globally consistent.

公开(公告)号：US11429841B1

公开(公告)日：2022-08-30

申请号：US16192437

申请日：2018-11-15

Applicant: Snap Inc.

Inventor： Jacob Minyoung Huh ,  Shao-Hua Sun ,  Ning Zhang

IPC: G06N3/04 ,  G06V30/19 ,  G06N3/08 ,  G06V30/194

Abstract: Disclosed is a feedback adversarial learning framework, a recurrent framework for generative adversarial networks that can be widely adapted to not only stabilize training but also generate higher quality images. In some aspects, a discriminator's spatial outputs are distilled to improve generation quality. The disclosed embodiments model the discriminator into the generator, and the generator learns from its mistakes over time. In some aspects, a discriminator architecture encourages the model to be locally and globally consistent.

公开(公告)号：US11604963B2

公开(公告)日：2023-03-14

申请号：US17808274

申请日：2022-06-22

Applicant: Snap Inc.

Inventor： Jacob Minyoung Huh ,  Shao-Hua Sun ,  Ning Zhang

IPC: G06N3/04 ,  G06V30/194 ,  G06N3/08

Abstract: Disclosed is a feedback adversarial learning framework, a recurrent framework for generative adversarial networks that can be widely adapted to not only stabilize training but also generate higher quality images. In some aspects, a discriminator's spatial outputs are distilled to improve generation quality. The disclosed embodiments model the discriminator into the generator, and the generator learns from its mistakes over time. In some aspects, a discriminator architecture encourages the model to be locally and globally consistent.