发表机构
Tandon School of Engineering, New York University（纽约大学坦登工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在动态计算环境下的预算图像分类问题，提出将其形式化为资源分配整数规划。针对原规划NP难问题，先提出连续松弛的内容无关策略，又改进为内容敏感策略，经实验验证性能更优，还从理论上研究策略并分析失败案例以指导未来研究。
AI 中文摘要
人工智能的广泛应用需要在多种计算环境中部署深度神经网络。我们考虑计算需求会变化的动态环境，提出问题：如何调整人工智能分类系统的复杂度，在满足变化的计算约束时最大化其准确率？我们将此问题称为预算图像分类，并将其形式化为资源分配整数规划。给定计算预算、一批图像和一个能以不同复杂度决策的分类系统，探索将图像分配到决策点的策略以在可用预算内最大化准确率。原整数规划是NP难的，我们提出连续松弛得到内容无关分配策略，又提出内容敏感策略，实验表明其性能更优。我们从理论上研究策略行为，推导决策点适合预算分类须满足的条件，分析失败案例以提供未来研究方向的见解。
英文摘要
The ever-growing adoption of Artificial Intelligence (AI) creates the need to deploy Deep Neural Networks in a variety of computational environments. We consider dynamic environments, where computational requirements are subject to change, and we pose the following question: How do we adjust the complexity of an AI classification system, in order to maximize its accuracy, while meeting changing computational constraints? We call this problem Budgeted Image Classification, and we formally formulate it as a resource allocation integer program. Given a computational budget, a batch of images, and a classification system that can make decisions with varying complexity (it has multiple decision points), we explore strategies to allocate images to decision points, in order to maximize accuracy within the available budget. The original integer program is NP-Hard, so, we propose a continuous relaxation, leading to a content-agnostic allocation strategy which assigns images to decision points without considering their particular content. We address this issue by proposing a content-sensitive strategy, that we experimentally show it leads to superior performance. We theoretically study the behavior of our strategies, deriving conditions that must be satisfied by decision points to be suitable for budgeted classification. We analyze fails cases, offering insights for future research directions.
