no code implementations • 15 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.
no code implementations • 25 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.
1 code implementation • 7 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.
no code implementations • 12 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.
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.
no code implementations • 21 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.
1 code implementation • 21 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.
1 code implementation • 19 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.
no code implementations • 17 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.
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.
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.
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.
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.
no code implementations • 21 Dec 2019 • Deli Zhao, Jiapeng Zhu, Bo Zhang
Variational Auto-Encoder (VAE) has been widely applied as a fundamental generative model in machine learning.
no code implementations • 25 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.
no code implementations • 25 Sep 2019 • Deli Zhao, Jiapeng Zhu, Bo Zhang
Variational inference is a fundamental problem in Variational AutoEncoder (VAE).
no code implementations • 27 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.
3 code implementations • 19 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.