<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>

CC BY-SA 4.0
2020-08-10T20:05:45.707Z
2020-08-10T20:05:45.707Z
matt
<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>

CC BY-SA 4.0
2020-09-22T22:10:23.127Z
2020-09-22T22:10:23.127Z
Yasser.AbdelSattar
<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>

<transfer-learning>
What is layer freezing in transfer learning?
Pluviophile
# 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


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## 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.)


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### 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.


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