Large-scale, high-quality data are considered an essential factor for the
successful application of many deep learning techniques. Meanwhile, numerous
real-world deep learning tasks still have to contend with the lack of
sufficient amounts of high-quality data. Additionally, issues such as model
robustness, fairness, and trustworthiness are also closely related to training
data. Consequently, a huge number of studies in the existing literature have
focused on the data aspect in deep learning tasks. Some typical data
optimization techniques include data augmentation, logit perturbation, sample
weighting, and data condensation. These techniques usually come from different
deep learning divisions and their theoretical inspirations or heuristic
motivations may seem unrelated to each other. This study aims to organize a
wide range of existing data optimization methodologies for deep learning from
the previous literature, and makes the effort to construct a comprehensive
taxonomy for them. The constructed taxonomy considers the diversity of split
dimensions, and deep sub-taxonomies are constructed for each dimension. On the
basis of the taxonomy, connections among the extensive data optimization
methods for deep learning are built in terms of four aspects. We probe into
rendering several promising and interesting future directions. The constructed
taxonomy and the revealed connections will enlighten the better understanding
of existing methods and the design of novel data optimization techniques.
Furthermore, our aspiration for this survey is to promote data optimization as
an independent subdivision of deep learning. A curated, up-to-date list of
resources related to data optimization in deep learning is available at
https://github.com/YaoRujing/Data-Optimization.

通过对现有文献中的数据优化方法进行分类整理和建立联系，本研究旨在构建一个全面的深度学习数据优化方法分类体系，并探索未来的研究方向。

深度学习中的数据优化：一项调查

Data Optimization in Deep Learning: A Survey

Satisfaction measurement, which emerges in every sector today, is a very
important factor for many companies. In this study, it is aimed to reach the
highest accuracy rate with various machine learning algorithms by using the
data on Yemek Sepeti and variations of this data. The accuracy values of each
algorithm were calculated together with the various natural language processing
methods used. While calculating these accuracy values, the parameters of the
algorithms used were tried to be optimized. The models trained in this study on
labeled data can be used on unlabeled data and can give companies an idea in
measuring customer satisfaction. It was observed that 3 different natural
language processing methods applied resulted in approximately 5% accuracy
increase in most of the developed models.

使用 Yemek Sepeti 的数据和其变体，采用多种机器学习算法，结合多种自然语言处理方法，旨在达到最高的准确率，并优化算法的参数，训练得到的模型可用于无标签数据的客户满意度测量。本研究中应用的三种自然语言处理方法能够使大部分模型的准确率提高约 5%。

基于机器学习方法的土耳其情感分析：在线食品订单网站评论应用

Turkish Sentiment Analysis Using Machine Learning Methods: Application on Online Food Order Site Reviews

The efficacy of machine learning (ML) models depends on both algorithms and
data. Training data defines what we want our models to learn, and testing data
provides the means by which their empirical progress is measured. Benchmark
datasets define the entire world within which models exist and operate, yet
research continues to focus on critiquing and improving the algorithmic aspect
of the models rather than critiquing and improving the data with which our
models operate. If "data is the new oil," we are still missing work on the
refineries by which the data itself could be optimized for more effective use.

这篇论文探讨了机器学习模型的有效性，强调了数据的重要性以及对数据进行优化以提高模型效果的必要性，并指出需要将关注点从算法改进转移到数据的优化。