Towards interpretable deep learning with limited examples

现有的视觉概念检测系统不能检测日常生活中不断演变的概念。这个项目的目的是提取描述视觉概念语义的模式，并开发或调整知识转移学习技术，以适应有限示例的新概念。预期的结果将为构建高效、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：具有交流机制的可解释深度学习。这一新领域及其技术将帮助我们识别滥用家庭患者医疗设备和未经授权的活动，并使我们能够制定有效的应对措施，以防止网络攻击。

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.