Search Results for author: Zhenliang He

Found 6 papers, 3 papers with code

Multimodal deep representation learning for quantum cross-platform verification

no code implementations7 Nov 2023 Yang Qian, Yuxuan Du, Zhenliang He, Min-Hsiu Hsieh, DaCheng Tao

Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms, utilizing minimal measurements.

Representation Learning

EigenGAN: Layer-Wise Eigen-Learning for GANs

1 code implementation ICCV 2021 Zhenliang He, Meina Kan, Shiguang Shan

Via generative adversarial training to learn a target distribution, these layer-wise subspaces automatically discover a set of "eigen-dimensions" at each layer corresponding to a set of semantic attributes or interpretable variations.

Attribute Face Generation +1

PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing

3 code implementations12 Jul 2020 Zhenliang He, Meina Kan, Jichao Zhang, Shiguang Shan

Facial attribute editing aims to manipulate attributes on the human face, e. g., adding a mustache or changing the hair color.

Attribute Generative Adversarial Network

S2GAN: Share Aging Factors Across Ages and Share Aging Trends Among Individuals

no code implementations ICCV 2019 Zhenliang He, Meina Kan, Shiguang Shan, Xilin Chen

Generally, we human follow the roughly common aging trends, e. g., the wrinkles only tend to be more, longer or deeper.

Face Age Editing

AttGAN: Facial Attribute Editing by Only Changing What You Want

10 code implementations29 Nov 2017 Zhenliang He, WangMeng Zuo, Meina Kan, Shiguang Shan, Xilin Chen

Based on the encoder-decoder architecture, facial attribute editing is achieved by decoding the latent representation of the given face conditioned on the desired attributes.

Attribute

Funnel-Structured Cascade for Multi-View Face Detection with Alignment-Awareness

no code implementations23 Sep 2016 Shuzhe Wu, Meina Kan, Zhenliang He, Shiguang Shan, Xilin Chen

On the other hand, by using a unified MLP cascade to examine proposals of all views in a centralized style, it provides a favorable solution for multi-view face detection with high accuracy and low time-cost.

Face Alignment Face Detection

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