Towards interpretable deep learning with limited examples

现有的视觉概念检测系统无法检测到日常生活中不断变化的概念。该项目旨在提取描述视觉概念语义的模式，并在有限的示例中开发或调整知识迁移学习技术以适应新概念。预期结果将为构建高效和可解释的视觉分析学习系统提供重大技术突破，并将开辟一个全新的研究方向：具有通信机制的可解释深度学习。这个新领域及其技术将帮助我们识别家庭患者医疗设备的滥用和未经授权的活动，并使我们能够制定有效的应对措施来防止网络攻击。

Existing visual concept detection systems are incapable of detecting ever-evolving concepts in daily life. This project aims to extract patterns that describe the semantics of visual concepts and to develop or adapt knowledge transfer learning technologies for new concepts with limited examples. The expected outcomes will provide major technological breakthroughs for building efficient and interpretable learning systems for visual analysis and will open an entirely new research direction: interpretable deep learning with communication mechanism. This new field and its technologies will help us to recognise misuse of home patient medical devices and unauthorised activity, and enable us to devise effective responses to prevent cyberattacks.