Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …

Text style transfer is an important task in natural language generation, which aims to control
certain attributes in the generated text, such as politeness, emotion, humor, and many …

Controllable image synthesis models allow creation of diverse images based on text
instructions or guidance from a reference image. Recently, denoising diffusion probabilistic …

We introduce a differentiable rasterizer that bridges the vector graphics and raster image
domains, enabling powerful raster-based loss functions, optimization procedures, and …

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks
(CNNs) in creating artistic imagery by separating and recombining image content and style …

A word-as-image is a semantic typography technique where a word illustration presents a
visualization of the meaning of the word, while also preserving its readability. We present a …

Voice cloning is a highly desired feature for personalized speech interfaces. We introduce a
neural voice cloning system that learns to synthesize a person's voice from only a few audio …

Large deep learning models are impressive, but they struggle when real-time data is not
available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for …

Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …