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Suppose, a deep learning classifier is trained to distinguish between images of cars, ships, trucks, birds, horses and dogs. But all the training data for the birds were yellow birds. Can the trained classifier then be used to only detect yellow birds within a birds image data set ? Image data is just an example here. The data can be other things like DNA sequences too. Please bear with me if the question is non-sensical or too basic. JavaScript questions and answers, JavaScript questions pdf, JavaScript question bank, JavaScript questions and answers pdf, mcq on JavaScript pdf, JavaScript questions and solutions, JavaScript mcq Test , Interview JavaScript questions, JavaScript Questions for Interview, JavaScript MCQ (Multiple Choice Questions)

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In the example which you mentioned you are not learning your classifier to discriminate cars, ships, trucks, birds, horses and dogs but between five first things you mentioned and yellow birds. This means that when the score out from birds unit - assuming that your model is performing well and your dataset was sufficiently large - then you might assume that it will be able to discriminate between different objects and yellow birds - also when these different objects would be other birds. Of course - there is some small probability that it will learn to discriminate among birds and different objects using only shapes - but it's too small in my opinion to be taken into account. Of course - you might check that by simply generating an appropriate testing dataset. In general - it depends on many factors. One of them is the architecture and design of your network. Discriminating yellow birds from different coloured one should be easy because of the convolution of coloured images nature. In different cases - it might not be so obvious. Other thing is how far conceptually are these classes which you want to discriminate from each other. If e.g. example - this other class can be build out of the same concepts as the learnt one - you might have problem - cause network might simply learn them as indicators of yellow birds. So the best thing to do is to design appropriate testing dataset and perform the comparision between scores of different classes. If you prove that this score performs well - then you are done. If not - you need to retrain your network.

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