Free Information Center
Neural processes are a class of meta-learning models that combine neural networks with stochastic processes to enable fast adaptation to new tasks. They provide a flexible and scalable approach for learning functions from limited data by modeling distributions over functions.
Few-shot learning is a machine learning approach that enables models to learn new tasks using only a small number of training examples. It addresses the challenge of data scarcity in traditional supervised learning by leveraging prior knowledge or meta-learning techniques to generalize from limited data.
Few-shot prompting is a technique in natural language processing where a language model is given a small number of example inputs and outputs to perform a task. This method enables models to generalize and complete tasks with limited examples, reducing the need for extensive task-specific training.
Few-shot text-to-speech (TTS) is an advanced approach in speech synthesis that enables the creation of natural-sounding voice models using only a small amount of reference audio data. This technique aims to generate high-quality speech in a target speaker’s voice after exposure to limited examples, facilitating rapid adaptation to new voices with minimal data.