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Yezhi Shu

2 accepted papers

2023

FEditNet: Few-Shot Editing of Latent Semantics in GAN Spaces

AAAI 2023technical

Generative Adversarial networks (GANs) have demonstrated their powerful capability of synthesizing high-resolution images, and great efforts have been made to interpret the semantics in the latent spaces of GANs. However, existing works still have the following limitations: (1) the majority of works…

2020

Towards Better Generalization: Joint Depth-Pose Learning Without PoseNet

CVPR 2020poster

In this work, we tackle the essential problem of scale inconsistency for self supervised joint depth-pose learning. Most existing methods assume that a consistent scale of depth and pose can be learned across all input samples, which makes the learning problem harder, resulting in degraded performan…

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