[null,null,["最后更新时间 (UTC)：2025-01-18。"],[[["\u003cp\u003eOverfitting in convolutional neural networks can be mitigated by using techniques like data augmentation and dropout regularization.\u003c/p\u003e\n"],["\u003cp\u003eData augmentation involves creating variations of existing training images to increase dataset diversity and size, which is particularly helpful for smaller datasets.\u003c/p\u003e\n"],["\u003cp\u003eDropout regularization randomly removes units during training to prevent the model from becoming overly specialized to the training data.\u003c/p\u003e\n"],["\u003cp\u003eWhen dealing with large datasets, the need for dropout regularization diminishes and the impact of data augmentation is reduced.\u003c/p\u003e\n"]]],[],null,[]]
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。