[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["没有我需要的信息","missingTheInformationINeed","thumb-down"],["太复杂/步骤太多","tooComplicatedTooManySteps","thumb-down"],["内容需要更新","outOfDate","thumb-down"],["翻译问题","translationIssue","thumb-down"],["示例/代码问题","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-01-18。"],[[["Overfitting in convolutional neural networks can be mitigated by using techniques like data augmentation and dropout regularization."],["Data augmentation involves creating variations of existing training images to increase dataset diversity and size, which is particularly helpful for smaller datasets."],["Dropout regularization randomly removes units during training to prevent the model from becoming overly specialized to the training data."],["When dealing with large datasets, the need for dropout regularization diminishes and the impact of data augmentation is reduced."]]],[]]
图 7. 单张狗狗图像的数据增强（摘自 Kaggle 上的“Dogs vs. Cats”数据集）。左图：训练集中的原始狗狗图像。右图：对原始图像进行随机转换后生成的九张新图。