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You are training a multimodal Generative A1 model for generating video captions. The model is overfitting to the training data, resulting in poor generalization to unseen videos. Which of the following regularization techniques would be MOST suitable to mitigate overfitting?
Correct Answer: A,B,C,D
Dropout randomly deactivates neurons during training, preventing the model from relying on specific neurons. L1 regularization adds a penalty to the loss function based on the absolute values of the weights, encouraging sparsity. Data augmentation increases the diversity of the training data. Early stopping prevents the model from training for too long and overfitting. Batch normalization primarily improves training stability and speed but also provides some regularization.