# Adaptive Age Estimation towards Imbalanced Datasets
## 2 Related Work  
The use of convolutional neural networks (CNNs) to extract age features from images has become increasingly popular, resulting in significant improvements in performance. For example, [PERSON] developed a shallow CNN architecture [21] and [PERSON] et al. [22] proposed a method using VGG-16. Recent works [23; 24; 25; 26] all use CNN to extract deep features for age estimation tasks. Previous papers [24; 27] categorized existing age estimation algorithms into different groups. Here, a review is given about age estimation techniques on our own. We propose that the existing methods for age estimation can be broadly classified into five main categories: multi-class classification, regression, label distribution learning, ranking, and hybrid (i.e., combining two or three different types of algorithms simultaneously). Classification-based methods consider ages as independent categories. DEX [19] is a typical classification method. It treats age estimation as a 101-class classification problem. Therefore, the penalty imposed on different error degrees is the same during the optimization process, which is unreasonable. For instance, the penalty imposed on wrongly predicting the age of 10 to 12 or to 20 should not be equivalent. Therefore, the relation between age labels is not exploited in multi-class classification works. Another restriction of multi-classification of age estimation is that the class imbalance in age facial datasets can result in an overfitting problem [23]. Utilizing a regressor to predict age is a common approach and can lead to improved accuracy. However, the aging process is inherently random and it may not be appropriate to apply a single pattern to fit real aging modes. Therefore, some studies have proposed the use of multiple local regressors, using a divide-and-conquer strategy to handle different age groups. However, this approach may ignore the continuous relationship between subsets and result in suboptimal performance. To address this issue, [PERSON] et al. proposed Bridgenet [24], which uses gating networks to enforce similarity between neighboring subset nodes and improve the overall performance of the age estimation model. Label-distribution learning approaches to age estimation [22; 28] were proposed to alleviate the disadvantages brought by insufficient training data with exact ages and group ambiguity. They represented each age as a distribution and KL divergence was applied to measure the similarity between a predicted distribution and ground-truth distribution. The training instances related to each age will be increased without an increase in the number of training samples. For example, [PERSON] et al. put forward mean-variance loss [29] for robust age estimation via distribution learning. To exploit the relative ordering of ages, ranking-based methods utilize several simple binary classifiers to judge the rank of a given facial image's age [25; 30; 31]. The final prediction is the combination of the results of sub-binary classifiers. For example, Ranking-CNN pre-trained several basic CNNs on a large dataset and fine-tuned them with ordinal age labels [32]. However, these methods ignore the relation between binary sub-problems. Hybrid methods are the combination of two or more algorithms. They have become popular in age estimation recently [33; 34; 35], because fusing two or three methods can bring complementary advantages. The research on imbalanced datasets can be roughly divided into the data-level and algorithm-level. Re-sampling is considered one of the primary methods for alleviating data imbalance in imbalanced datasets, addressing the issue from a data-centric perspective. Re-sampling is to adjust the training dataset in a proper way to make sure the sample quantity of the minority class and majority class in the training dataset is basically consistent. It can be roughly divided into over-sampling and under-sampling.