Autoencoder is an unsupervised learning model, which can automatically learn data
features from a large number of samples and can act as a dimensionality reduction method …

Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

Given observed samples x from a distribution of interest, the goal of a generative model is to
learn to model its true data distribution p (x). Once learned, we can generate new samples …

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-
Encoders (VAEs) are widely utilized to model the generative process of user interactions …

We present Bit Diffusion: a simple and generic approach for generating discrete data with
continuous state and continuous time diffusion models. The main idea behind our approach …

Diffusion-based generative models have demonstrated a capacity for perceptually
impressive synthesis, but can they also be great likelihood-based models? We answer this …

Score-based generative models (SGMs) have recently demonstrated impressive results in
terms of both sample quality and distribution coverage. However, they are usually applied …

Text-to-speech (TTS) has made rapid progress in both academia and industry in recent
years. Some questions naturally arise that whether a TTS system can achieve human-level …

Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural
speech given text, is a hot research topic in speech, language, and machine learning …