変化し続ける環境への逐次敵対的ドメイン適応
Incremental Adversarial Domain Adaptation for Continually Changing Environments
天候や照明の変化に応じてモデルを段階的に適応させる敵対的ドメイン適応手法を提案し、走行可能領域セグメンテーションで昼夜間の大きな見た目変化への頑健性を向上させた。
著者: Markus Wulfmeier, Alex Bewley, Ingmar Posner
分類: stat.ML, cs.CV, cs.RO
原文アブストラクト
Continuous appearance shifts such as changes in weather and lighting conditions can impact the performance of deployed machine learning models. While unsupervised domain adaptation aims to address this challenge, current approaches do not utilise the continuity of the occurring shifts. In particular, many robotics applications exhibit these conditions and thus facilitate the potential to incrementally adapt a learnt model over minor shifts which integrate to massive differences over time. Our work presents an adversarial approach for lifelong, incremental domain adaptation which benefits from unsupervised alignment to a series of intermediate domains which successively diverge from the labelled source domain. We empirically demonstrate that our incremental approach improves handling of large appearance changes, e.g. day to night, on a traversable-path segmentation task compared with a direct, single alignment step approach. Furthermore, by approximating the feature distribution for the source domain with a generative adversarial network, the deployment module can be rendered fully independent of retaining potentially large amounts of the related source training data for only a minor reduction in performance.
