Abstract
Neural Architecture Search (NAS) for deep learning object detection frameworks typically involves multiple modules, each performing distinct tasks. These modules contribute to a vast search space, resulting in searches that can take several GPU hours or even days, depending on the complexity of the search space. This makes joint optimization both challenging and computationally expensive. Furthermore, satisfying target device constraints across modules adds additional complexity to the optimization process. To address these challenges, we propose \textbf{FACETS}, e\textbf{\underline{F}}ficient Once-for-\textbf{\underline{A}}ll Object Detection via \textbf{\underline{C}}onstrained it\textbf{\underline{E}}ra\textbf{\underline{T}}ive\textbf{\underline{S}}earch, a novel unified iterative NAS method that refines the architecture of all modules in a cyclical manner. FACETS leverages feedback from previous iterations, alternating between fixing one module's architecture and optimizing the others. This approach reduces the overall search space while preserving interdependencies among modules and incorporates constraints based on the target device's computational budget. In a controlled comparison against progressive and single-module search strategies, FACETS achieves architectures with up to $4.75\%$ higher accuracy twice as fast as progressive search strategies in earlier stages, while still being able to achieve a global optimum. Moreover, FACETS demonstrates the ability to iteratively refine the search space, producing better performing architectures over time. The refined search space yields candidates with a mean accuracy up to $27\%$ higher than global search and $5\%$ higher than progressive search methods via random sampling.
Abstract (translated)
神经架构搜索（NAS）在深度学习目标检测框架中通常涉及多个执行不同任务的模块。这些模块共同构成了一个巨大的搜索空间，导致搜索过程可能需要数小时甚至几天的时间，具体取决于搜索空间的复杂性。这使得联合优化既具有挑战性又计算成本高昂。此外，在满足目标设备跨模块约束的情况下进行优化增加了额外的复杂度。为了解决这些问题，我们提出了**FACETS**（通过受限迭代搜索实现高效的一次性所有对象检测）这一新颖的统一迭代NAS方法，该方法在循环中对所有模块的架构进行精细化调整。 FACETS 利用前一次迭代的反馈，在每次迭代中交替固定一个模块的架构并优化其他模块。这种方法减少了总体搜索空间，同时保持了各模块之间的依赖关系，并根据目标设备的计算预算引入了约束条件。通过与渐进式和单一模块搜索策略进行受控对比实验，FACETS 在早期阶段实现了最高提升27%平均精度的目标架构，其准确性比渐进式搜索策略高出4.75%，速度却是后者的两倍，并且依然能够达到全局最优解。此外，在整个优化过程中，FACETS 有能力逐步细化搜索空间，从而随着时间的推移生成性能更好的架构。 具体而言，通过随机采样，FACETS 的精细化搜索空间产生的候选架构在平均准确度上比全球搜索方法高出27%，比渐进式搜索方法高5%。
URL
https://arxiv.org/abs/2503.21999
