Comparing different age estimation methods poses a challenge due to the
unreliability of published results, stemming from inconsistencies in the
benchmarking process. Previous studies have reported continuous performance
improvements over the past decade using specialized methods; however, our
findings challenge these claims. We argue that, for age estimation tasks
outside of the low-data regime, designing specialized methods is unnecessary,
and the standard approach of utilizing cross-entropy loss is sufficient. This
paper aims to address the benchmark shortcomings by evaluating state-of-the-art
age estimation methods in a unified and comparable setting. We systematically
analyze the impact of various factors, including facial alignment, facial
coverage, image resolution, image representation, model architecture, and the
amount of data on age estimation results. Surprisingly, these factors often
exert a more significant influence than the choice of the age estimation method
itself. We assess the generalization capability of each method by evaluating
the cross-dataset performance for publicly available age estimation datasets.
The results emphasize the importance of using consistent data preprocessing
practices and establishing standardized benchmarks to ensure reliable and
meaningful comparisons. The source code is available at
this https URL

对于年龄估计任务，设计专门的方法是不必要的，使用交叉熵损失的标准方法已足够。本文旨在通过在统一可比较的环境中评估最先进的年龄估计方法来解决基准测试的缺点。结果表明，与年龄估计方法的选择相比，面部对齐、面部覆盖、图像分辨率、图像表示、模型架构和数据量等各种因素经常产生更大的影响。评估了每种方法在公开可用的年龄估计数据集上的跨数据集性能，结果强调了使用一致的数据预处理方法和建立标准化基准测试的重要性，以确保可靠且有意义的比较。

揭开年龄估计之谜：基于深度学习方法的面部年龄估计的比较分析

Unraveling the Age Estimation Puzzle: Comparative Analysis of Deep  Learning Approaches for Facial Age Estimation

Strong empirical evidence that one machine-learning algorithm A outperforms
another one B ideally calls for multiple trials optimizing the learning
pipeline over sources of variation such as data sampling, data augmentation,
parameter initialization, and hyperparameters choices. This is prohibitively
expensive, and corners are cut to reach conclusions. We model the whole
benchmarking process, revealing that variance due to data sampling, parameter
initialization and hyperparameter choice impact markedly the results. We
analyze the predominant comparison methods used today in the light of this
variance. We show a counter-intuitive result that adding more sources of
variation to an imperfect estimator approaches better the ideal estimator at a
51 times reduction in compute cost. Building on these results, we study the
error rate of detecting improvements, on five different deep-learning
tasks/architectures. This study leads us to propose recommendations for
performance comparisons.

通过模拟对比机器学习算法的整个基准测试过程，我们发现数据采样、参数初始化和超参数选择对结果的影响显著。进一步分析今天所用的主要比较方法，我们提出一种反直觉的结果，即在不增加计算成本的情况下，将更多的变化源添加到不完美的估计器中可以接近更理想的估计器。通过五个不同的深度学习任务和架构，分析改进检测错误率，提出了性能比较的建议。