Search Results for author: Jiapeng Zhu

Found 18 papers, 7 papers with code

Class-Balanced and Reinforced Active Learning on Graphs

no code implementations15 Feb 2024 Chengcheng Yu, Jiapeng Zhu, Xiang Li

It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes.

Active Learning Graph Classification +2

In-Domain GAN Inversion for Faithful Reconstruction and Editability

no code implementations25 Sep 2023 Jiapeng Zhu, Yujun Shen, Yinghao Xu, Deli Zhao, Qifeng Chen, Bolei Zhou

This work fills in this gap by proposing in-domain GAN inversion, which consists of a domain-guided encoder and a domain-regularized optimizer, to regularize the inverted code in the native latent space of the pre-trained GAN model.

Image Generation Image Reconstruction

Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis

1 code implementation7 Sep 2023 Jiapeng Zhu, Ceyuan Yang, Kecheng Zheng, Yinghao Xu, Zifan Shi, Yujun Shen

Due to the difficulty in scaling up, generative adversarial networks (GANs) seem to be falling from grace on the task of text-conditioned image synthesis.

Image Generation Philosophy +1

GH-Feat: Learning Versatile Generative Hierarchical Features from GANs

no code implementations12 Jan 2023 Yinghao Xu, Yujun Shen, Jiapeng Zhu, Ceyuan Yang, Bolei Zhou

In this work we investigate that such a generative feature learned from image synthesis exhibits great potentials in solving a wide range of computer vision tasks, including both generative ones and more importantly discriminative ones.

Face Verification Image Harmonization +3

LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesis

no code implementations ICCV 2023 Jiapeng Zhu, Ceyuan Yang, Yujun Shen, Zifan Shi, Bo Dai, Deli Zhao, Qifeng Chen

This work presents an easy-to-use regularizer for GAN training, which helps explicitly link some axes of the latent space to a set of pixels in the synthesized image.

Image Generation

Interpreting Class Conditional GANs with Channel Awareness

no code implementations21 Mar 2022 Yingqing He, Zhiyi Zhang, Jiapeng Zhu, Yujun Shen, Qifeng Chen

To describe such a phenomenon, we propose channel awareness, which quantitatively characterizes how a single channel contributes to the final synthesis.

High-fidelity GAN Inversion with Padding Space

1 code implementation21 Mar 2022 Qingyan Bai, Yinghao Xu, Jiapeng Zhu, Weihao Xia, Yujiu Yang, Yujun Shen

In this work, we propose to involve the padding space of the generator to complement the latent space with spatial information.

Generative Adversarial Network Image Manipulation +1

Region-Based Semantic Factorization in GANs

1 code implementation19 Feb 2022 Jiapeng Zhu, Yujun Shen, Yinghao Xu, Deli Zhao, Qifeng Chen

Despite the rapid advancement of semantic discovery in the latent space of Generative Adversarial Networks (GANs), existing approaches either are limited to finding global attributes or rely on a number of segmentation masks to identify local attributes.

3D-Aware Indoor Scene Synthesis with Depth Priors

no code implementations17 Feb 2022 Zifan Shi, Yujun Shen, Jiapeng Zhu, Dit-yan Yeung, Qifeng Chen

In this way, the discriminator can take the spatial arrangement into account and advise the generator to learn an appropriate depth condition.

3D-Aware Image Synthesis Indoor Scene Synthesis

One-Shot Generative Domain Adaptation

no code implementations ICCV 2023 Ceyuan Yang, Yujun Shen, Zhiyi Zhang, Yinghao Xu, Jiapeng Zhu, Zhirong Wu, Bolei Zhou

We then equip the well-learned discriminator backbone with an attribute classifier to ensure that the generator captures the appropriate characters from the reference.

Attribute Domain Adaptation +1

Low-Rank Subspaces in GANs

1 code implementation NeurIPS 2021 Jiapeng Zhu, Ruili Feng, Yujun Shen, Deli Zhao, ZhengJun Zha, Jingren Zhou, Qifeng Chen

Concretely, given an arbitrary image and a region of interest (e. g., eyes of face images), we manage to relate the latent space to the image region with the Jacobian matrix and then use low-rank factorization to discover steerable latent subspaces.

Attribute Generative Adversarial Network

Generative Hierarchical Features from Synthesizing Images

1 code implementation CVPR 2021 Yinghao Xu, Yujun Shen, Jiapeng Zhu, Ceyuan Yang, Bolei Zhou

Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data.

Face Verification Image Classification +2

In-Domain GAN Inversion for Real Image Editing

2 code implementations ECCV 2020 Jiapeng Zhu, Yujun Shen, Deli Zhao, Bolei Zhou

A common practice of feeding a real image to a trained GAN generator is to invert it back to a latent code.

Image Reconstruction

Latent Variables on Spheres for Autoencoders in High Dimensions

no code implementations21 Dec 2019 Deli Zhao, Jiapeng Zhu, Bo Zhang

Variational Auto-Encoder (VAE) has been widely applied as a fundamental generative model in machine learning.

Vocal Bursts Intensity Prediction

Latent Variables on Spheres for Sampling and Inference

no code implementations25 Sep 2019 Deli Zhao, Jiapeng Zhu, Bo Zhang

Variational inference is a fundamental problem in Variational AutoEncoder (VAE).

Variational Inference

LIA: Latently Invertible Autoencoder with Adversarial Learning

no code implementations25 Sep 2019 Jiapeng Zhu, Deli Zhao, Bolei Zhou, Bo Zhang

A two-stage stochasticity-free training scheme is designed to train LIA via adversarial learning, in the sense that the decoder of LIA is first trained as a standard GAN with the invertible network and then the partial encoder is learned from an autoencoder by detaching the invertible network from LIA.

Decoder Generative Adversarial Network +1

Curriculum Learning for Deep Generative Models with Clustering

no code implementations27 Jun 2019 Deli Zhao, Jiapeng Zhu, Zhenfang Guo, Bo Zhang

The experiments on cat and human-face data validate that our algorithm is able to learn the optimal generative models (e. g. ProGAN) with respect to specified quality metrics for noisy data.

Clustering Generative Adversarial Network

Disentangled Inference for GANs with Latently Invertible Autoencoder

3 code implementations19 Jun 2019 Jiapeng Zhu, Deli Zhao, Bo Zhang, Bolei Zhou

In this paper, we show that the entanglement of the latent space for the VAE/GAN framework poses the main challenge for encoder learning.


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