there is no magic in Neural Network except it works in very high dimensional simplex plane for classification or regression (ultimately it’s a linear function fit and region cut). If you are so lucky that your validation dataset happens to be linearly “perpendicular” to your training dataset in the high dimensional space. Then no matter how  you train your model, the validation result will be poor by definition. To prove the point, you might swap your validation set and training set and see the phenomena… (well the above thought is just a conjecture not a theorem for your case, because I have very little information about your model… ; ))