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In case of CNN, you are correct in the sense that you cannot use the final layer weights if the number of categories are different. But you CAN reuse the weights in the initial layers. These recognise the lower level objects in the image. There is no need to train all over again. You would only have to train the upper layers specific to the categorisation ...


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If we classify new objects using transfer learning: 1.We delete the top Dense layer of the pre trained neural network. 2.Now suppose you have to classify 5 classes, so your final dense layer will contain 5 nodes. 3.Also you will add some dense layers prior to your new 5 node Dense layer, so that you can train the model with new data. 4.All the layers prior ...


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By default, BERT fine-tuning involves learning a task-specific layer (For classification task, a neural network on top of the CLS token), as well as update the existing parameters of the model to adapt for the task. Thus, it's both, new layer + BERT model weights. However, you still have a choice of using just the emebdding of CLS token and train only the ...


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