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I trained a convolutional neural network with a large (>90k) dataset of images. Since there is some confusion between two classes, I collected all the images that were misclassified and I augmented them with several methods. Furthermore, I collected new images that were not present in the original dataset. Now I would like to re-train my network in order to reduce the confusion. What is the best approach? Should I perform fine-tuning using only the new images (case A) or should I add the new images to the whole training set and re-train the network from zero (case B)?

My concerns are the following: in case A, most of the images belong to the same class, therefore the dataset would be very unbalanced. In case B, I am afraid that the impact of the new images on the whole dataset would be very small.

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I guess you can try both the solutions.

First try fine-tuning. Then, if the results aren't improving, retrain with all the data.

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  • $\begingroup$ This is generally the best answer whenever a data science issues throws up "should I try model or training variation A or B". There is some small chance someone familiar enough with the problem has done similar enough work (similar data, models and problem), and they would have a clue. Otherwise, it is time to find out by experimentation. In this case, the OP has not given enough data for any expert to make a decent guess. $\endgroup$ – Neil Slater Sep 24 '18 at 16:19
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    $\begingroup$ You could detail a bit more in this answer what trying both solutions entails. The OP does need to be aware of using cross-validation so that they can compare approaches for instance, and it is not clear from the question that they are doing that. $\endgroup$ – Neil Slater Sep 24 '18 at 16:22
  • $\begingroup$ Thank you. I added some details in order to express my concerns better $\endgroup$ – firion Sep 25 '18 at 8:59

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