Why is my compressed Convolutional Neural Network (CNN) showing poor performance after projection in MATLAB R2023b?
I have trained a convolutional neural network (CNN) and a fully connected network (FCN) on the same task. I would like to apply projection to reduce the size and computation needed for these networks. Then I would like to fine-tune these networks after projection to ensure that their performance is high.
I referenced this documentation page to understand how projection works:
https://www.mathworks.com/company/technical-articles/compressing-neural-networks-using-network-projection.html
and referenced this documentation example while projecting my models:
https://www.mathworks.com/help/releases/R2023b/deeplearning/ug/compress-neural-network-using-projection.html
Here is the documentation for the projection function I am using:
https://www.mathworks.com/help/releases/R2023b/deeplearning/ref/compressnetworkusingprojection.html
I find that the projected and fine-tuned CNN experiences large performance degradation compared to the unprojected CNN even with a small learnable reduction during projection, as measured by the loss function of the network. This is a problem because I cannot use the projected and fine-tuned CNN with such low performance. I find that the projected and fine-tuned FC network does not exhibit such a large performance degradation as compared to the unprojected FC network.
I also find that, during the CNN fine-tuning process, the training and validation losses diverge.
Is the performance degradation of the projected and fine-tuned CNN expected?
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