title: Why test accuracy remains constant and do not increase in binary classification when test and train dataset are from different source
body: <p>I have <strong><em>train</em></strong> dataset and <strong><em>test</em></strong> dataset from two different sources. I mean they are from two different experiments but the results of both of them are same biological images. I want to do binary classification using <strong><em>deep CNN</em></strong> and I have following results on test accuracy and train accuracy. The blue line shows train accuracy and the red line shows test accuracy after almost <em>250</em> epochs. Why the test accuracy is almost constant and not raising?  Is that because <strong><em>Test</em></strong> and <strong><em>Train</em></strong> dataset are come from different distributions?<a href="https://i.stack.imgur.com/gqcdU.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/gqcdU.png" alt="enter image description here"></a></p>
  
  <p>Edited:
  After I have add dropout layer, reguralization terms and mean subtraction I still get following strange results which says the model is overfitting from the beginning!</p>
  
  <p><a href="https://i.stack.imgur.com/iexkF.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/iexkF.png" alt="enter image description here"></a>
  <a href="https://i.stack.imgur.com/BpviO.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/BpviO.png" alt="enter image description here"></a></p>