论文标题
图像字幕的交互式机器学习
Interactive Machine Learning for Image Captioning
论文作者
论文摘要
我们为图像字幕模型提出了一种交互式学习的方法。由于人类反馈是昂贵且基于神经网络的方法，通常需要大量的监督数据进行培训，因此我们设想了一种系统，通过使用数据增强方法将反馈乘以反馈，并将结果培训示例集成到模型中。该方法具有三个关键组件，我们需要找到合适的实践实现：反馈收集，数据增强和模型更新。我们概述了我们的想法并查看解决这些任务的不同可能性。
We propose an approach for interactive learning for an image captioning model. As human feedback is expensive and modern neural network based approaches often require large amounts of supervised data to be trained, we envision a system that exploits human feedback as good as possible by multiplying the feedback using data augmentation methods, and integrating the resulting training examples into the model in a smart way. This approach has three key components, for which we need to find suitable practical implementations: feedback collection, data augmentation, and model update. We outline our idea and review different possibilities to address these tasks.
