augmentation

augmentation的音标为[ˌɔːgməˈteɪʃn]，基本翻译为“增加；增大；扩充”。

速记技巧：可以将该单词拆分记忆，其中augment是词干部分，表示“增加”，-ation是名词后缀。可以联想与之相似的单词“enlarge”（扩大）。

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Augmentation的英文词源是拉丁语augere，意为“增加”。其变化形式主要有augmented（增加的）、augmentation（增加）和augment（增加）。

相关单词：

1. superfluity（过剩）：指超过生活需要的多余之物，其词源正是拉丁语的super（超过）和fluere（流动）。这个词可以形容某物过多以至于不需要，或者某人的财富或能力过剩。

2. multiplication（乘法）：源自拉丁语的multiplicare，意为“使增加”，是augmentation的反义词。这个词表示数量或数量的增加。

3. augmenting（增强）：指通过增加、改进或增强某物来提高其性能或能力。这个词通常用于描述技术、工程或军事方面的操作。

augmentation在英语中是一个常用的术语，用于描述通过增加某种资源或能力来提高效果或性能。同时，augment一词也常用于描述技术、工程或商业方面的增长和发展。这些词汇都反映了augmentation一词在英语中的广泛应用和重要性。

常用短语：

1. data augmentation

2. image augmentation

3. feature augmentation

4. text augmentation

5. data enhancement

6. dataset augmentation

7. sample augmentation

双语例句：

1. Data augmentation can significantly improve the accuracy of machine learning models. (数据增强可以显著提高机器学习模型的准确性)

2. Image augmentation is a commonly used technique in machine vision. (图像增强是机器视觉中常用的一种技术)

3. Feature augmentation can help improve the robustness of machine learning models in complex environments. (特征增强可以帮助提高机器学习模型在复杂环境中的鲁棒性)

4. Text augmentation can enhance the quality of text-based machine learning models. (文本增强可以提高基于文本的机器学习模型的性能)

5. By using data enhancement techniques, we can improve the quality and accuracy of our datasets. (通过使用数据增强技术，我们可以提高数据集的质量和准确性)

6. Dataset augmentation is essential for training deep learning models that are robust and accurate. (数据集增强对于训练稳健且准确的深度学习模型至关重要)

7. Sample augmentation can significantly improve the performance of machine learning models in practical applications. (样本增强可以在实际应用中显著提高机器学习模型的性能)

英文小作文：

Augmentation is a crucial technique in machine learning that can significantly improve the accuracy and robustness of machine learning models. Data augmentation, image augmentation, feature augmentation, text augmentation, data enhancement, dataset augmentation, and sample augmentation are all techniques that can be used to enhance the quality of machine learning models. For example, data augmentation can be used to generate additional training data by using simple transformations on existing data, which can help improve the accuracy of machine learning models. Similarly, image augmentation can be used to generate additional images by applying simple transformations to existing images, which can help improve the performance of image-based machine learning models. Feature augmentation can be used to enhance the features of machine learning models by generating additional features from existing features, which can help improve the accuracy and robustness of machine learning models in complex environments. In conclusion, augmentation techniques are essential for improving the performance and accuracy of machine learning models and should be used regularly in machine learning projects.