We are currenyly working with BIG 2015 dataset, where we have created black and white images using the OP code files given. Then we have used a generator to create similar looking images to the actual dataset images. We have used discriminator in tandem to improved the generated images. Now we have to create a CNN to which is trained on original + generated images, then classify unseen data into 10 classes of malwares.
The CNN should have pre-trained weights from discriminator and I am looking for a way on how to do that?
Our images are 10873, 128×128 images which are B/W.
We have trained our discriminator and basically added the layers into CNN (not weights). And we are getting accuracy of 30%. We need help to reduce the overfitting as the train-test split is giving 96% accuracy.
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