When training a Deep Learning model on a relatively small dataset, it happens that the validation loss is lower than the training loss while the test loss is higher than both.More concrete, in terms of accuracy, the training and validation accuracies approach 99% while the test one flattens at 68%. What would be the causes and how could it be avoided?
user
Nice question! There could be several reasons why your validation loss is lower than your training loss. One reason could be that you have over-regularized your model. You can try relaxing your regularization constraints by lowering your L2 weight decay strength, reducing the amount of dropout you’re applying, or increasing your model capacity (i.e., make it deeper). Another reason could be due to dropout not being applied during validation.

I hope this helps!
assistant
Interesting, can you explain that but in simpler terms so someone who's not informed on the matter could understand?
user
Sure, I'd be happy to explain in simpler terms!

When you train a machine learning model, you're trying to teach it to make accurate predictions. To do this, you give it lots of examples and let it learn from them. The model learns by adjusting its internal parameters so that it can make better predictions.

During training, the model sees lots of examples and tries to adjust its parameters to make better predictions on those examples. But the model hasn't seen the validation examples before, so it might not do as well on them.

If your validation loss is lower than your training loss, it means that the model is doing better on the validation examples than it is on the training examples. This can happen for a few reasons.

One reason is that you may have over-regularized your model. Regularization is a technique used to prevent overfitting, which is when a model is too complex and starts to memorize the training examples instead of learning the underlying patterns. Over-regularization can make the model too simple and prevent it from learning important patterns in the data.

Another reason could be due to dropout not being applied during validation. Dropout is a technique used to prevent overfitting by randomly dropping out some of the neurons in the model during training. This can help the model learn more robust features. However, during validation, all neurons are used, which can lead to better performance.

To address these issues, you can try adjusting the regularization constraints, changing the amount of dropout, or increasing the model capacity (making it deeper or wider). This can help the model learn more complex patterns and perform better on both training and validation examples.
assistant