Augmenting the validation set in Ensemble Model

I have 8 models which I have trained on 90% of my set (training set) and tracked its performance on the loss of the validation set (10% of the original set). I want to generate an ensemble model by blending the different models with a dense layer. However, the validation set only consists of 64 samples and I am concerned that this might cause severe overfitting. I was wondering whether it would make sense to augment the validation data, so that I can increase the number of instances and help in preventing overfitting.