From an engineering perspective, a design should not only perform well in an
ideal condition, but should also resist noises. Such a design methodology,
namely robust design, has been widely implemented in the industry for product
quality control. However, classic robust design requires a lot of evaluations
for a single design target, while the results of these evaluations could not be
reused for a new target. To achieve a data-efficient robust design, we propose
Robust Inverse Design under Noise (RID-Noise), which can utilize existing noisy
data to train a conditional invertible neural network (cINN). Specifically, we
estimate the robustness of a design parameter by its predictability, measured
by the prediction error of a forward neural network. We also define a
sample-wise weight, which can be used in the maximum weighted likelihood
estimation of an inverse model based on a cINN. With the visual results from
experiments, we clearly justify how RID-Noise works by learning the
distribution and robustness from data. Further experiments on several
real-world benchmark tasks with noises confirm that our method is more
effective than other state-of-the-art inverse design methods. Code and
supplementary is publicly available at
this https URL

本论文提出了一种数据高效的稳健反向设计（Robust Inverse Design under Noise，RID-Noise）方法，其利用现有的有噪数据训练条件可逆神经网络（cINN）来估算设计参数的稳健性，并定义一种样本权重，在基于 cINN 的反向模型的最大加权似然估计中使用。实验结果表明，该方法在多个真实世界基准任务中比其他最先进的反向设计方法更有效。

RID-Noise：面向噪声环境下的稳健逆向设计

RID-Noise: Towards Robust Inverse Design under Noisy Environments

Face detection is a well-explored problem. Many challenges on face detectors
like extreme pose, illumination, low resolution and small scales are studied in
the previous work. However, previous proposed models are mostly trained and
tested on good-quality images which are not always the case for practical
applications like surveillance systems. In this paper, we first review the
current state-of-the-art face detectors and their performance on benchmark
dataset FDDB, and compare the design protocols of the algorithms. Secondly, we
investigate their performance degradation while testing on low-quality images
with different levels of blur, noise, and contrast. Our results demonstrate
that both hand-crafted and deep-learning based face detectors are not robust
enough for low-quality images. It inspires researchers to produce more robust
design for face detection in the wild.

这篇论文回顾了目前最先进的人脸检测器在高质量图像上的表现，并评估了它们在低质量图像上的鲁棒性，结果表明，手工设计或基于深度学习的人脸检测器在低质量图像上的性能下降，需要研究更加鲁棒的设计方案。