5 citations · 7 across the 13 of their papers we have counts for
13 papers
Image Tokenizer Needs Post-Training
Kai Qiu, Xiang Li, Hao Chen +7
Recent image generative models typically capture the image distribution in a pre-constructed latent space, relying on a frozen image tokenizer. However, there exists a significant…
Robust Latent Matters: Boosting Image Generation with Sampling Error Synthesis
Kai Qiu, Xiang Li, Jason Kuen +7
Recent image generation schemes typically capture image distribution in a pre-constructed latent space relying on a frozen image tokenizer. Though the performance of tokenizer play…
CAARMA: Class Augmentation with Adversarial Mixup Regularization
Massa Baali, Xiang Li, Hao Chen +3
Speaker verification is a typical zero-shot learning task, where inference of unseen classes is performed by comparing embeddings of test instances to known examples. The models pe…
Masked Autoencoders Are Effective Tokenizers for Diffusion Models
Hao Chen, Yujin Han, Fangyi Chen +7
Recent advances in latent diffusion models have demonstrated their effectiveness for high-resolution image synthesis. However, the properties of the latent space from tokenizer for…
XQ-GAN: An Open-source Image Tokenization Framework for Autoregressive Generation
Xiang Li, Kai Qiu, Hao Chen +5
Image tokenizers play a critical role in shaping the performance of subsequent generative models. Since the introduction of VQ-GAN, discrete image tokenization has undergone remark…
SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer
Hao Chen, Ze Wang, Xiang Li +7
Efficient image tokenization with high compression ratios remains a critical challenge for training generative models. We present SoftVQ-VAE, a continuous image tokenizer that leve…
Perturbation Ontology based Graph Attention Networks
Yichen Wang, Jie Wang, Fulin Wang +3
In recent years, graph representation learning has undergone a paradigm shift, driven by the emergence and proliferation of graph neural networks (GNNs) and their heterogeneous cou…
On the Diversity of Synthetic Data and its Impact on Training Large Language Models
Hao Chen, Abdul Waheed, Xiang Li +4
The rise of Large Language Models (LLMs) has accentuated the need for diverse, high-quality pre-training data. Synthetic data emerges as a viable solution to the challenges of data…
ImageFolder: Autoregressive Image Generation with Folded Tokens
Xiang Li, Kai Qiu, Hao Chen +4
Image tokenizers are crucial for visual generative models, e.g., diffusion models (DMs) and autoregressive (AR) models, as they construct the latent representation for modeling. In…
Efficient Autoregressive Audio Modeling via Next-Scale Prediction
Kai Qiu, Xiang Li, Hao Chen +5
Audio generation has achieved remarkable progress with the advance of sophisticated generative models, such as diffusion models (DMs) and autoregressive (AR) models. However, due t…
ControlVAR: Exploring Controllable Visual Autoregressive Modeling
Xiang Li, Kai Qiu, Hao Chen +4
Conditional visual generation has witnessed remarkable progress with the advent of diffusion models (DMs), especially in tasks like control-to-image generation. However, challenges…
Slight Corruption in Pre-training Data Makes Better Diffusion Models
Hao Chen, Yujin Han, Diganta Misra +6
Diffusion models (DMs) have shown remarkable capabilities in generating realistic high-quality images, audios, and videos. They benefit significantly from extensive pre-training on…
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models
Manuel Schürch, Xiang Li, Ahmed Allam +4
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient…