I have read about one-class classification problems using SVM as discriminator to detect anomalous data. But, couldn't find much with CNN deep networks. I am working on a classification problem where I want to extract features from CNN network. I am providing image dataset from one class only and after training the model for 50 epochs, I am extracting the features for further classification using another model.
I am getting very fine results. However, I am confused how my CNN model is learning features when I am providing data from one class only. Is it even learning or not. I tried to analyze activations after each convolution layer (4 in my model) for 1, 50 and 100 epochs. I can see subtle changes, however I need a solid explanation as to what actually is happening after each epoch cycle.
for epoch 1 and conv layer 1,2 ,3 and 4 , strongest activations in each layer is as shown below:
after 50 epochs and conv layer 1,2 ,3 and 4, strongest activations in each layer is as shown below:
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