Answers.Body: <p>The <a href="https://dictionary.cambridge.org/dictionary/english/off-the-shelf" rel="nofollow noreferrer">dictionary's definition of off-the-shelf</a> is</p>
  <blockquote>
  <p>used to describe a product that is available immediately and does not need to be specially made to suit a particular purpose</p>
  </blockquote>
  <p>The same dictionary provides several examples</p>
  <blockquote>
  <p>You can purchase off-the-shelf software or have it customized to suit your needs.</p>
  <p>If you have complex needs, we don't recommend that you buy software off the shelf.</p>
  <p>For this, off-the-shelf algorithms included in the robot's programming libraries are used.</p>
  </blockquote>
  <p>So, your intuition is correct! An off-the-shelf model, software, product, etc., is any model, software or, respectively, product that would be easily (or immediately) available or applicable to the specific context, but, at the same time, it may also be applicable to many other contexts or problems.</p>
  <p>An off-the-shelf convolutional neural network is thus a typical or standard CNN that can be applied immediately in that context (but that is potentially applicable to many other contexts or problems). Examples of CNNs that could be used as off-the-shelf models are <a href="https://en.wikipedia.org/wiki/AlexNet" rel="nofollow noreferrer">AlexNet</a> or <a href="http://yann.lecun.com/exdb/lenet/" rel="nofollow noreferrer">LeNet-5</a>, but the actual choice depends on the context and needs.</p>
  <p>An off-the-shelf model can also be a <em>baseline model</em> (i.e. a very simple model that is used in experiments as the model that every other model should outperform), but not necessarily.</p>
  <p>Alex Graves uses this term/expression in his paper <a href="https://papers.nips.cc/paper/4329-practical-variational-inference-for-neural-networks.pdf" rel="nofollow noreferrer">Practical Variational Inference for Neural Networks</a>.</p>
  
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Answers.Text: In the context of the linked paper, it looks like the modern term for what they are doing is transfer learning - taking a pre-trained CNN for large scale image task and using it for something else by swapping out the last layer or two (or simply pre-processing the images by collecting the last feature layer from them before feeding to new ML). That is probably the more popular term, and maybe worth linking to something explaining that for the OP.
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Answers.Text: @NeilSlater Ok, I will maybe read that paper, but I think that this doesn't change the main meaning of "off-the-shelf". If I understand what you're saying, they are still using an "off-the-shelf" model (although a trained one). I don't think "off-the-shelf" is only used in the context of transfer learning. I am sure I've seen it being used in other contexts too (but I admit I would need to search for the specific papers).
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Body: <p>I encountered the phrase/concept <em>off-the-shelf CNN</em> in <a href="https://arxiv.org/abs/1403.6382" rel="nofollow noreferrer">this paper</a> in which authors used <em>off-the-shelf CNN</em> representation, <code>OverFeat</code>, with simple classifiers to address different recognition tasks.</p>
  
  <p>As I understand it correctly, it literally means something is ready to be used for a task without alteration.</p>
  
  <p>Can somebody explain in simple words what <em>off-the-shelf CNN</em> technically means in the context of AI and convolutional neural networks?</p>
  
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Comments.CreationDate: 2020-04-08T09:39:18.193Z
Comments.Text: I've encounter this term in couple of papers, usually they propose concepts where a component can be replaced by any existing model. For example if we think about GANs (generative adversarial nets) they proposed adversarial training where each of their components, the generator and the discriminator, could be off the self CNN. That means that we could take any CNN network which is suitable for the task we are trying to solve, for example AlexNet, and just plug it in.
Comments.UserDisplayName: razvanc92
Tags: <machine-learning><convolutional-neural-networks><computer-vision><terminology>
Title: What does "off-the-shelf" mean?
UserDisplayName: doplano
ThreadText: # What does "off-the-shelf" mean?
  
  (Asked by: username_0 on 2020-04-08)
  
  I encountered the phrase/concept *off-the-shelf CNN* in [this paper](https://arxiv.org/abs/1403.6382) in which authors used *off-the-shelf CNN* representation, `OverFeat`, with simple classifiers to address different recognition tasks.
  
  As I understand it correctly, it literally means something is ready to be used for a task without alteration.
  
  Can somebody explain in simple words what *off-the-shelf CNN* technically means in the context of AI and convolutional neural networks?
  
  **Tags:** machine-learning, convolutional-neural-networks, computer-vision, terminology
  
  > **Comments:**
  > **username_1**: I've encounter this term in couple of papers, usually they propose concepts where a component can be replaced by any existing model. For example if we think about GANs (generative adversarial nets) they proposed adversarial training where each of their components, the generator and the discriminator, could be off the self CNN. That means that we could take any CNN network which is suitable for the task we are trying to solve, for example AlexNet, and just plug it in.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2020-04-08. Score: 1 | Accepted answer
  
  The [dictionary's definition of off-the-shelf](https://dictionary.cambridge.org/dictionary/english/off-the-shelf) is
  
  > used to describe a product that is available immediately and does not need to be specially made to suit a particular purpose
  
  The same dictionary provides several examples
  
  > You can purchase off-the-shelf software or have it customized to suit your needs.
  >
  > If you have complex needs, we don't recommend that you buy software off the shelf.
  >
  > For this, off-the-shelf algorithms included in the robot's programming libraries are used.
  
  So, your intuition is correct! An off-the-shelf model, software, product, etc., is any model, software or, respectively, product that would be easily (or immediately) available or applicable to the specific context, but, at the same time, it may also be applicable to many other contexts or problems.
  
  An off-the-shelf convolutional neural network is thus a typical or standard CNN that can be applied immediately in that context (but that is potentially applicable to many other contexts or problems). Examples of CNNs that could be used as off-the-shelf models are [AlexNet](https://en.wikipedia.org/wiki/AlexNet) or [LeNet-5](http://yann.lecun.com/exdb/lenet/), but the actual choice depends on the context and needs.
  
  An off-the-shelf model can also be a *baseline model* (i.e. a very simple model that is used in experiments as the model that every other model should outperform), but not necessarily.
  
  Alex Graves uses this term/expression in his paper [Practical Variational Inference for Neural Networks](https://papers.nips.cc/paper/4329-practical-variational-inference-for-neural-networks.pdf).
  
  > **Comments:**
  > **username_3**: In the context of the linked paper, it looks like the modern term for what they are doing is transfer learning - taking a pre-trained CNN for large scale image task and using it for something else by swapping out the last layer or two (or simply pre-processing the images by collecting the last feature layer from them before feeding to new ML). That is probably the more popular term, and maybe worth linking to something explaining that for the OP.  
  > **username_2**: @username_3 Ok, I will maybe read that paper, but I think that this doesn't change the main meaning of "off-the-shelf". If I understand what you're saying, they are still using an "off-the-shelf" model (although a trained one). I don't think "off-the-shelf" is only used in the context of transfer learning. I am sure I've seen it being used in other contexts too (but I admit I would need to search for the specific papers).  
  
  ---
  
  