ARC Future Fellowships - Grant ID: FT210100228

用于目标识别的自动样本学习。该项目旨在使计算机能够学习如何有效地使用训练样本进行目标识别。训练样本是计算机学习识别对象的唯一来源。该项目开创了一个新的研究方向，将使首次全面探索样本的力量成为可能。这些目标将通过利用强化学习、快速训练算法和开发新的深度学习算法的最新进展来实现。新算法将有利于广泛的应用，例如，有效地使用汽车碰撞训练样本来准确识别运输中潜在的道路碰撞，以及有效地使用稀有的医学成像训练数据来稳健地诊断健康中的疾病。

AUSLearn: AUtomated Sample Learning for Object Recognition. This project aims to enable computers to learn how to effectively use training samples for object recognition. Training sample is the only source used by computers to learn recognising objects. This project creates a new research direction that will enable the first full exploration of the power of samples. The aims will be enabled by leveraging the recent advances in reinforcement learning, fast training algorithms, and by developing novel deep learning algorithms. The new algorithms will benefit a wide range of applications, e.g. to effectively use car crash training samples for accurately identifying potential road crashes in transport and to effectively use rare medical imaging training data for robustly diagnosing diseases in health.