The extraction of multi-attribute objects from the deep web is the bridge between the unstructured web and structured data. Existing approaches either induce wrappers from a set of human-annotated pages or leverage repeated structures on the page without supervision. What the former lack in automation, the latter lack in accuracy. Thus accurate, automatic multi-attribute object extraction has remained an open challenge. AMBER overcomes both limitations through mutual supervision between the repeated structure and automatically produced annotations. Previous approaches based on automatic annotations have suffered from low quality due to the inherent noise in the annotations and have attempted to compensate by exploring multiple candidate wrappers. In contrast, AMBER compensates for this noise by integrating repeated structure analysis with annotation-based induction: The repeated structure limits the search space for wrapper induction, and conversely, annotations allow the repeated structure analysis to distinguish noise from relevant data. Both, low recall and low precision in the annotations are mitigated to achieve almost human quality (more than 98 percent) multi-attribute object extraction. To achieve this accuracy, AMBER needs to be trained once for an entire domain. AMBER bootstraps its training from a small, possibly noisy set of attribute instances and a few unannotated sites of the domain.