37 citations · 64 across the 10 of their papers we have counts for
10 papers
Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free Approach
Yufan Zhou, Ruiyi Zhang, Tong Sun +1
Recent text-to-image generation models have demonstrated impressive capability of generating text-aligned images with high fidelity. However, generating images of novel concept pro…
Shifted Diffusion for Text-to-image Generation
Yufan Zhou, Bingchen Liu, Yizhe Zhu +3
We present Corgi, a novel method for text-to-image generation. Corgi is based on our proposed shifted diffusion model, which achieves better image embedding generation from input t…
Lafite2: Few-shot Text-to-Image Generation
Yufan Zhou, Chunyuan Li, Changyou Chen +2
Text-to-image generation models have progressed considerably in recent years, which can now generate impressive realistic images from arbitrary text. Most of such models are traine…
A Generic Approach for Enhancing GANs by Regularized Latent Optimization
Yufan Zhou, Chunyuan Li, Changyou Chen +1
With the rapidly growing model complexity and data volume, training deep generative models (DGMs) for better performance has becoming an increasingly more important challenge. Prev…
LAFITE: Towards Language-Free Training for Text-to-Image Generation
Yufan Zhou, Ruiyi Zhang, Changyou Chen +6
One of the major challenges in training text-to-image generation models is the need of a large number of high-quality image-text pairs. While image samples are often easily accessi…
Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernels
Yufan Zhou, Changyou Chen, Jinhui Xu
Learning high-dimensional distributions is an important yet challenging problem in machine learning with applications in various domains. In this paper, we introduce new techniques…
Meta-Learning with Neural Tangent Kernels
Yufan Zhou, Zhenyi Wang, Jiayi Xian +2
Model Agnostic Meta-Learning (MAML) has emerged as a standard framework for meta-learning, where a meta-model is learned with the ability of fast adapting to new tasks. However, as…
Learning Manifold Implicitly via Explicit Heat-Kernel Learning
Yufan Zhou, Changyou Chen, Jinhui Xu
Manifold learning is a fundamental problem in machine learning with numerous applications. Most of the existing methods directly learn the low-dimensional embedding of the data in…
Graph Neural Networks with Composite Kernels
Yufan Zhou, Jiayi Xian, Changyou Chen +1
Learning on graph structured data has drawn increasing interest in recent years. Frameworks like Graph Convolutional Networks (GCNs) have demonstrated their ability to capture stru…
KernelNet: A Data-Dependent Kernel Parameterization for Deep Generative Modeling
Yufan Zhou, Changyou Chen, Jinhui Xu
Learning with kernels is an important concept in machine learning. Standard approaches for kernel methods often use predefined kernels that require careful selection of hyperparame…