Yufan Zhou | DeepAI

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∙ 06/29/2023

LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

Instruction tuning unlocks the superior capability of Large Language Mod...

0 Yanzhe Zhang, et al. ∙

∙ 05/23/2023

Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free Approach

Recent text-to-image generation models have demonstrated impressive capa...

0 Yufan Zhou, et al. ∙

∙ 12/25/2022

Learning to Estimate 3D Human Pose from Point Cloud

3D pose estimation is a challenging problem in computer vision. Most of ...

0 Yufan Zhou, et al. ∙

∙ 11/24/2022

Shifted Diffusion for Text-to-image Generation

We present Corgi, a novel method for text-to-image generation. Corgi is ...

0 Yufan Zhou, et al. ∙

∙ 10/25/2022

Lafite2: Few-shot Text-to-Image Generation

Text-to-image generation models have progressed considerably in recent y...

0 Yufan Zhou, et al. ∙

∙ 12/07/2021

A Generic Approach for Enhancing GANs by Regularized Latent Optimization

With the rapidly growing model complexity and data volume, training deep...

0 Yufan Zhou, et al. ∙

∙ 11/27/2021

LAFITE: Towards Language-Free Training for Text-to-Image Generation

One of the major challenges in training text-to-image generation models ...

9 Yufan Zhou, et al. ∙

∙ 05/10/2021

Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernels

Learning high-dimensional distributions is an important yet challenging ...

7 Yufan Zhou, et al. ∙

∙ 02/07/2021

Meta-Learning with Neural Tangent Kernels

Model Agnostic Meta-Learning (MAML) has emerged as a standard framework ...

0 Yufan Zhou, et al. ∙

∙ 11/01/2020

MixKD: Towards Efficient Distillation of Large-scale Language Models

Large-scale language models have recently demonstrated impressive empiri...

0 Kevin J Liang, et al. ∙

∙ 10/05/2020

Learning Manifold Implicitly via Explicit Heat-Kernel Learning

Manifold learning is a fundamental problem in machine learning with nume...

0 Yufan Zhou, et al. ∙

∙ 05/16/2020

Graph Neural Networks with Composite Kernels

Learning on graph structured data has drawn increasing interest in recen...

0 Yufan Zhou, et al. ∙

∙ 12/21/2019

Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

Human-motion generation is a long-standing challenging task due to the r...

9 Zhenyi Wang, et al. ∙

∙ 12/02/2019

KernelNet: A Data-Dependent Kernel Parameterization for Deep Generative Modeling

Learning with kernels is an often resorted tool in modern machine learni...

24 Yufan Zhou, et al. ∙

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