<p>To rephrase your question:</p>

<p>"When training a neural network, a common stopping criterion is the 'early stopping criterion' which stops training when the validation loss increases (signaling overfitting). For small datasets, where training samples are precious, we would prefer to use some other criterion and use 100% of the data for training the model."</p>

<p>I think this is generally a hard problem, so I am not surprised you have not found a simple answer. I think you have a few options:</p>

<ol>
<li>Add regularization (such as Dropout or Batch Normalization) which should help prevent overfitting. Then, use the training loss for a stopping criterion. You could see how this approach would perform on a validation set without using early stopping to ensure that the model is not overfitting.</li>
<li>Be sure not to overprovision the model. Smaller models will have a more difficult time overfitting.</li>
<li>Take a look at the stopping criterion described in this paper which does not rely on a validation set: <a href="https://arxiv.org/pdf/1703.09580.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1703.09580.pdf</a></li>
</ol>

<p>Finally, you may not use Neural Networks here. Generally, these models work best with large amounts of training data. In this case of 700 samples, you can possibly get better performance with another algorithm.</p>
How to train the final Neural Network model after cross validation?
<p>This is a problem that I am constantly facing, but don't seem to find the answer anywhere. I have a data set of 700 samples. As a result, I have to use cross-validation instead of just using one validation and one test set to get a close estimate of the error.</p>

<p>I would like to use a neural network to do this. But after doing CV with a neural network, and get an error estimate, how do I train the NN on the whole data set? Because for other algorithms like Logistic regression or SVM, there is no question of when to stop in training. But for NN, you train it until your validation score goes down. So, for the final  model, training on the whole dataset, how do you know when to stop?</p>

<p>Just to make it clear, my problem is not how to choose hyper-parametes with NN. I can do that by using a nested CV. My question is how to train the final NN on the whole data set(when to stop more specifically) before applying it in wild?</p>