Unsupervised Scene Adaptation with Memory Regularization in vivo

Understand

We consider the unsupervised scene adaptation problem of learning from both labeled source data and unlabeled target data.

• Existing methods focus on minoring the inter-domain gap between the source and target domains.
• However, the intra-domain knowledge and inherent uncertainty learned by the network are under-explored.
• In this paper, we propose an orthogonal method, called memory regularization in vivo to exploit the intra-domain knowledge and regularize the model training.