"subagents," one for each subproblem, and then combine their solutions to establish

On one hand, the knowledge learned from public data is implicitly transferred into the private model.

Advances in in computer vision have lead to classifiers that nearly match human performance in many applications. However, while the human visual system is remarkably versatile in extracting semantic meaning out of even degraded and heavily obfuscated images, today's visual classifiers significantly lag behind in emulating the same robustness, and often yield incorrect outputs in the presence of natural and adversarial degradations.