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I encountered the phrase/concept off-the-shelf CNN in this paper 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?

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  • $\begingroup$ It is not a standardised term, so what is meant in detail will depend on the source. Could you please link to where you have seen it, and quote the sentence to give context? $\endgroup$ – Neil Slater Apr 8 at 7:32
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    $\begingroup$ I have updated my question with research paper $\endgroup$ – Färid Alijani Apr 8 at 7:39
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    $\begingroup$ 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. $\endgroup$ – razvanc92 Apr 8 at 9:39
  • $\begingroup$ @razvanc92 Your explanation seems consistent with my knowledge. I encourage you to provide a formal answer below! $\endgroup$ – nbro Apr 8 at 13:35
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The dictionary's definition of 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 or LeNet-5, 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.

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    $\begingroup$ 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. $\endgroup$ – Neil Slater Apr 8 at 15:32
  • $\begingroup$ @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). $\endgroup$ – nbro Apr 8 at 15:51

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