Answers.Body: <p><strong>Why is this layer freezing required?</strong></p>
  <p>It's not.</p>
  <p><strong>What are the effects of layer freezing?</strong>
  The consequences are:</p>
  <p>(1) Should be <em>faster</em> to train (the gradient will have far less components)</p>
  <p>(2) Should require <em>less</em> data to train on</p>
  <p>If you do unfreeze the weights, I'd think your performance would be better because you are adjusting (i.e., fine-tuning) the parameters to your specific problem at hand. I am not sure what the marginal improvements are in practice, as I have not experiemented much with fine-tuning (like are the improvements typically a 0.01% reduction in error rate? Not sure.)</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-08-10T20:05:45.707Z
Answers.LastActivityDate: 2020-08-10T20:05:45.707Z
Answers.UserDisplayName: matt
Answers.Body: <p>Layer freezing means that the layer weights of the trained model do not change when reused on a subsequent downstream mission, they remain frozen. Basically, when backpropagation is performed during training, these layer weights aren't compromised.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-09-22T22:10:23.127Z
Answers.LastActivityDate: 2020-09-22T22:10:23.127Z
Answers.UserDisplayName: Yasser.AbdelSattar
Body: <p>Transfer learning consists of taking features learned on one problem and leveraging them on a new, similar problem.</p>
  <p>In the Transfer Learning, we take layers from a previously trained model and freeze them.</p>
  <p><strong>Why is this layer freezing required and what are the effects of layer freezing?</strong></p>
  
Tags: <transfer-learning>
Title: What is layer freezing in transfer learning?
UserDisplayName: Pluviophile
ThreadText: # What is layer freezing in transfer learning?
  
  (Asked by: username_0 on 2020-08-10)
  
  Transfer learning consists of taking features learned on one problem and leveraging them on a new, similar problem.
  
  In the Transfer Learning, we take layers from a previously trained model and freeze them.
  
  **Why is this layer freezing required and what are the effects of layer freezing?**
  
  **Tags:** transfer-learning
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2020-08-10. Score: 1 | Accepted answer
  
  **Why is this layer freezing required?**
  
  It's not.
  
  **What are the effects of layer freezing?**
  The consequences are:
  
  (1) Should be *faster* to train (the gradient will have far less components)
  
  (2) Should require *less* data to train on
  
  If you do unfreeze the weights, I'd think your performance would be better because you are adjusting (i.e., fine-tuning) the parameters to your specific problem at hand. I am not sure what the marginal improvements are in practice, as I have not experiemented much with fine-tuning (like are the improvements typically a 0.01% reduction in error rate? Not sure.)
  
  
  ---
  
  ### Answer by username_2 on 2020-09-22. Score: 0
  
  Layer freezing means that the layer weights of the trained model do not change when reused on a subsequent downstream mission, they remain frozen. Basically, when backpropagation is performed during training, these layer weights aren't compromised.
  
  
  ---
  
  