论文标题
与松散耦合样品适应显式域
Explicit Domain Adaptation with Loosely Coupled Samples
论文作者
论文摘要
转移学习是整个机器学习的重要领域，尤其是在完全自主驾驶的背景下，需要同时解决许多不同的领域，例如改变天气状况和特定国家 /地区的驾驶行为。传统的转移学习方法通常集中在图像数据上，并且是黑框模型。在这项工作中，我们提出了一个转移学习框架，其核心是学习域之间的明确映射。由于其可解释性，这对安全至关重要的应用是有益的，例如自动驾驶。我们通过考虑图像分类问题，然后继续进行时间序列数据，尤其是预测车道变化来显示其一般适用性。在我们的评估中，我们将预训练的模型调整到具有不同驾驶和感觉特征的数据集中。
Transfer learning is an important field of machine learning in general, and particularly in the context of fully autonomous driving, which needs to be solved simultaneously for many different domains, such as changing weather conditions and country-specific driving behaviors. Traditional transfer learning methods often focus on image data and are black-box models. In this work we propose a transfer learning framework, core of which is learning an explicit mapping between domains. Due to its interpretability, this is beneficial for safety-critical applications, like autonomous driving. We show its general applicability by considering image classification problems and then move on to time-series data, particularly predicting lane changes. In our evaluation we adapt a pre-trained model to a dataset exhibiting different driving and sensory characteristics.
