9 papers
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…
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…
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…
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…
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…
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…