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Control recognition bounds for visual learning and exploration

机译:控制视觉学习和探索的识别范围

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We describe tradeoffs between the performance in visual decision problems and the control authority that the agent can exercise on the sensing process. We focus on problems of “coverage” (ensuring that all regions in the scene are seen) and “change estimation” (finding and learning an unknown object in an otherwise known and static scene), propose a measure of control authority and empirically relate it to the expected risk and its proxy (conditional entropy of the posterior density). We then show that a “passive” agent can provide no guarantees on performance beyond what is afforded by the priors, and that an “omnipotent” agent, capable of infinite control authority, can achieve arbitrarily good performance (asymptotically).
机译:我们描述了视觉决策问题中的性能与代理可以在感知过程中行使的控制权之间的权衡。我们关注“覆盖”(确保可以看到场景中的所有区域)和“变化估计”(在已知的静态场景中发现和学习未知对象)的问题,提出控制权的度量并凭经验进行关联预期风险及其代理(后验密度的条件熵)。然后,我们证明“被动”代理不能提供超出先验条件所能提供的性能保证,并且“无所不能”代理能够无限地控制权限,可以(渐近地)实现任意良好的性能。

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