Search Results for author: Bingchen Liu

Found 12 papers, 6 papers with code

Diffusion Guided Domain Adaptation of Image Generators

no code implementations8 Dec 2022 Kunpeng Song, Ligong Han, Bingchen Liu, Dimitris Metaxas, Ahmed Elgammal

Can a text-to-image diffusion model be used as a training objective for adapting a GAN generator to another domain?

Domain Adaptation

Shifted Diffusion for Text-to-image Generation

no code implementations24 Nov 2022 Yufan Zhou, Bingchen Liu, Yizhe Zhu, Xiao Yang, Changyou Chen, Jinhui Xu

Unlike the baseline diffusion model used in DALL-E 2, our method seamlessly encodes prior knowledge of the pre-trained CLIP model in its diffusion process by designing a new initialization distribution and a new transition step of the diffusion.

Zero-Shot Text-to-Image Generation

PIVQGAN: Posture and Identity Disentangled Image-to-Image Translation via Vector Quantization

no code implementations29 Sep 2021 Bingchen Liu, Yizhe Zhu, Xiao Yang, Ahmed Elgammal

The VQSN module facilitates a more delicate separation of posture and identity, while the training scheme ensures the VQSN module learns the pose-related representations.

Disentanglement Image-to-Image Translation +2

Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis

6 code implementations ICLR 2021 Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal

Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images.

Image Generation

Self-Supervised Sketch-to-Image Synthesis

1 code implementation16 Dec 2020 Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal

Moreover, with the proposed sketch generator, the model shows a promising performance on style mixing and style transfer, which require synthesized images to be both style-consistent and semantically meaningful.

Image Generation Self-Supervised Learning +1

TIME: Text and Image Mutual-Translation Adversarial Networks

no code implementations27 May 2020 Bingchen Liu, Kunpeng Song, Yizhe Zhu, Gerard de Melo, Ahmed Elgammal

Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework.

Image Captioning Language Modelling +2

Sketch-to-Art: Synthesizing Stylized Art Images From Sketches

1 code implementation26 Feb 2020 Bingchen Liu, Kunpeng Song, Ahmed Elgammal

We propose a new approach for synthesizing fully detailed art-stylized images from sketches.

OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization

1 code implementation26 May 2019 Bingchen Liu, Yizhe Zhu, Zuohui Fu, Gerard de Melo, Ahmed Elgammal

Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regularization (OOGAN).


Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning

1 code implementation ICCV 2019 Yizhe Zhu, Jianwen Xie, Bingchen Liu, Ahmed Elgammal

We investigate learning feature-to-feature translator networks by alternating back-propagation as a general-purpose solution to zero-shot learning (ZSL) problems.

Zero-Shot Learning

CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms

9 code implementations21 Jun 2017 Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, Marian Mazzone

We argue that such networks are limited in their ability to generate creative products in their original design.

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