Overfitting in small datasets can be mitigated by employing data augmentation to artificially increase dataset diversity. Techniques like rotation, scaling, or flipping for images or synonym replacement in text help the model generalize better.
Regularization techniques like dropout, L2 weight decay, and early stopping limit the model's complexity, preventing it from memorizing the training data. Reducing the number of layers or neurons also curbs overfitting.
Transfer learning is highly effective for small datasets. By fine-tuning a pretrained model, you leverage existing knowledge from large datasets, improving generalization and reducing the risk of overfitting.