Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

As information exists in various modalities in real world, effective interaction and fusion
among multimodal information plays a key role for the creation and perception of multimodal …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

This survey reviews text-to-image diffusion models in the context that diffusion models have
emerged to be popular for a wide range of generative tasks. As a self-contained work, this …

A popular approach to protein design is to combine a generative model with a discriminative
model for conditional sampling. The generative model samples plausible sequences while …

We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation.
This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent …

We combine neural rendering with multi-modal image and text representations to synthesize
diverse 3D objects solely from natural language descriptions. Our method, Dream Fields …

We consider the problem of inferring high-dimensional data $ x $ in a model that consists of
a prior $ p (x) $ and an auxiliary differentiable constraint $ c (x, y) $ on $ x $ given some …

Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in
unconditional image generation. However, due to the stochasticity of the generative process …

Diffusion models generate images by iterative denoising. Recent work has shown that by
making the denoising process deterministic, one can encode real images into latent codes …