Answers.Body: <p>It is not advisable because if you use an &quot;other&quot; class, you are just increasing problems for your network. Since &quot;other&quot; means not dog and not cat, then, what common feature does it have? Most of the time the &quot;other&quot; images won't have many features in common. If they do, then go ahead and make an &quot;other&quot; class.</p>
  <p>There is a better way: if the probabilities for both cat and dog are less than a threshold (you need to decide that, take, 0.5), then, you can say it is an &quot;other&quot; object.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-03-09T10:37:15.250Z
Answers.Text: Thank you. If I know that some image categories (for ex. horses) will appear often but they are not of practical interest (my goal is to only tag Cats and Dogs), does it in that case make sense for model performance to make a third category?
Answers.UserDisplayName: Ruthger Righart
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Answers.CreationDate: 2021-03-09T10:45:04.390Z
Answers.Text: It only makes sense if the third class has visual features similar to the classes of interest. Since, it will be a source of confusion in decision, it makes sense to make the network to learn to separate them out. On the other hand, if confusion is happening then you can increase the threshold for classification.
Answers.UserDisplayName: Abhishek Verma
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Answers.CreationDate: 2021-03-09T10:26:16.820Z
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Answers.UserDisplayName: Abhishek Verma
Body: <p>In image classification, there are sometimes images that do not fit in any category.</p>
  <p>For example, if I build a CNN in Keras to classify Dogs and Cats, does it help (in terms of training time and performance) to create an &quot;other&quot; (or &quot;unclassified&quot;) category in which images of houses, people, birds, etc., are classified? Is there any research paper that discusses this?</p>
  <p>A similar question was asked before <a href="https://ai.stackexchange.com/q/24914/2444">here</a>, but, unfortunately, it has no answer.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-03-09T12:26:43.183Z
Comments.Text: Related question and my answer: https://ai.stackexchange.com/questions/17721/why-do-cnns-sometimes-make-highly-confident-mistakes-and-how-can-one-combat-th/17722#17722 basically you need to use a Bayesian neural network which produces good epistemic uncertainty in order to do out of distribution detection
Comments.UserDisplayName: Dr. Snoopy
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Comments.CreationDate: 2021-03-10T00:38:04.833Z
Comments.Text: Other similar questions have been asked here in the past: [this](https://ai.stackexchange.com/q/4889/2444), [this](https://ai.stackexchange.com/q/18745/2444) or [this](https://ai.stackexchange.com/q/26175/2444).
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-03-10T06:50:29.867Z
Comments.Text: Does this answer your question? [In a CNN, does each new filter have different weights for each input channel, or are the same weights of each filter used across input channels?](https://ai.stackexchange.com/questions/5769/in-a-cnn-does-each-new-filter-have-different-weights-for-each-input-channel-or)
Comments.UserDisplayName: Pluviophile
Tags: <convolutional-neural-networks><computer-vision><keras><image-recognition><reference-request>
Title: Should one use an "other" category in image classification?
UserDisplayName: Ruthger Righart
ThreadText: # Should one use an "other" category in image classification?
  
  (Asked by: username_0 on 2021-03-09)
  
  In image classification, there are sometimes images that do not fit in any category.
  
  For example, if I build a CNN in Keras to classify Dogs and Cats, does it help (in terms of training time and performance) to create an "other" (or "unclassified") category in which images of houses, people, birds, etc., are classified? Is there any research paper that discusses this?
  
  A similar question was asked before [here](https://ai.stackexchange.com/q/24914/2444), but, unfortunately, it has no answer.
  
  **Tags:** convolutional-neural-networks, computer-vision, keras, image-recognition, reference-request
  > **Comments:**
  > **username_1**: Related question and my answer: https://ai.stackexchange.com/questions/17721/why-do-cnns-sometimes-make-highly-confident-mistakes-and-how-can-one-combat-th/17722#17722 basically you need to use a Bayesian neural network which produces good epistemic uncertainty in order to do out of distribution detection  
  > **username_2**: Other similar questions have been asked here in the past: [this](https://ai.stackexchange.com/q/4889/2444), [this](https://ai.stackexchange.com/q/18745/2444) or [this](https://ai.stackexchange.com/q/26175/2444).  
  > **username_3**: Does this answer your question? [In a CNN, does each new filter have different weights for each input channel, or are the same weights of each filter used across input channels?](https://ai.stackexchange.com/questions/5769/in-a-cnn-does-each-new-filter-have-different-weights-for-each-input-channel-or)  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_4 on 2021-03-09. Score: 1 | Accepted answer
  
  It is not advisable because if you use an "other" class, you are just increasing problems for your network. Since "other" means not dog and not cat, then, what common feature does it have? Most of the time the "other" images won't have many features in common. If they do, then go ahead and make an "other" class.
  
  There is a better way: if the probabilities for both cat and dog are less than a threshold (you need to decide that, take, 0.5), then, you can say it is an "other" object.
  
  > **Comments:**
  > **username_0**: Thank you. If I know that some image categories (for ex. horses) will appear often but they are not of practical interest (my goal is to only tag Cats and Dogs), does it in that case make sense for model performance to make a third category?  
  > **username_4**: It only makes sense if the third class has visual features similar to the classes of interest. Since, it will be a source of confusion in decision, it makes sense to make the network to learn to separate them out. On the other hand, if confusion is happening then you can increase the threshold for classification.  
  
  ---
  
  