<p>Yes this is standard transfer learning. Using a trained model, we can freeze the first N hidden layers of a classifier, except for the last few. This will allow our previous relevant training to be retained whilst also being able learn new features and target new classes.</p>
<p>We will then initialize a new output layer that works our new context(i.e sigmoid, 1 node for binary classifier). Everything is now set to resume training on our new y_targets.</p>
<p>Take a look at <a href="https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a" rel="nofollow noreferrer">this link</a> for some more info on transfer learning.</p>
<p>If you want no perturbance to your past learning, I would recommend freezing all previous hidden layers and then tacking some additional ones on.</p>

CC BY-SA 4.0
2019-04-07T18:01:25.240Z
But doesn't fine-tuning require even the old objects annotated in the new training images? I'm trying to avoid having to annotate the old objects in the new images.
John M.
CC BY-SA 4.0
2019-04-06T23:52:04.330Z
2019-04-06T23:52:04.330Z
2020-06-17T09:57:20.710Z
hisairnessag3
<p>Suppose a model <code>M</code> classifies apples and oranges. Can <code>M</code> be extended to classify a third class of objects, e.g., pears, such that the new images for 'retraining' only have pears annotated and apples and oranges ignored? That is, since <code>M</code> already classifies apples and oranges, can the old weights be somehow preserved and let the retraining focus specifically on learning about pears?</p>

<p>Methods such as fine-tuning and learning without forgetting seem to require all objects in the new images annotated though.</p>

<neural-networks><deep-learning><convolutional-neural-networks>
Extending a neural network to classify new objects
John M.
# Extending a neural network to classify new objects

(Asked by: username_0 on 2019-04-05)

Suppose a model `M` classifies apples and oranges. Can `M` be extended to classify a third class of objects, e.g., pears, such that the new images for 'retraining' only have pears annotated and apples and oranges ignored? That is, since `M` already classifies apples and oranges, can the old weights be somehow preserved and let the retraining focus specifically on learning about pears?

Methods such as fine-tuning and learning without forgetting seem to require all objects in the new images annotated though.

**Tags:** neural-networks, deep-learning, convolutional-neural-networks


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## 1 answers

### Answer by username_1 on 2019-04-06. Score: 2

Yes this is standard transfer learning. Using a trained model, we can freeze the first N hidden layers of a classifier, except for the last few. This will allow our previous relevant training to be retained whilst also being able learn new features and target new classes.

We will then initialize a new output layer that works our new context(i.e sigmoid, 1 node for binary classifier). Everything is now set to resume training on our new y\_targets.

Take a look at [this link](https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a) for some more info on transfer learning.

If you want no perturbance to your past learning, I would recommend freezing all previous hidden layers and then tacking some additional ones on.

> **Comments:**
> **username_0**: But doesn't fine-tuning require even the old objects annotated in the new training images? I'm trying to avoid having to annotate the old objects in the new images.  

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