[null,null,["最終更新日 2025-01-18 UTC。"],[[["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."]]],[]]
図 7. 1 匹の犬の画像に対するデータ拡張（Kaggle で入手可能な 「犬と猫」データセットから抜粋）。左: トレーニング セットの元の犬の画像。右: ランダムな変換を使用して元の画像から生成された 9 つの新しい画像。