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Im trying to make a dark image brighter using CNN-UNet arcitecture.

When I train the network I get the following results: enter image description here

When I cut the features in half for pruning, and do full train again, I get the following resuls: enter image description here

There are those bubble artifacts near the light.

Why they are happening?

1)How can I locate the problametic neurons that cause it?

2)Can I train the network(change the loss or anything) to not generate those made up bubbles?

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Did you retrain the network after pruning the weights?

If not, 1) the bubbles are probably appearing because of that, and 2) you definitely should retrain your network.

Did you prune output features only or weights inside the network also?

The standard procedure is to retrain the network after pruning, and the best results can be achieved with iterative pruning: you prune small part of weights (the loss of accyracy is inevitable here), then retrain to get the accuracy back, and repeat until you won't be able to get the accuracy back to high level.

Here Pruning Convolutional Neural Networks for Resource Efficient Inference is an article about (among others) iterative pruning.

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  • $\begingroup$ I do full training and get those results. They occur becuase I changed number of channels from 16 to 8 in the network. $\endgroup$ – BestR May 14 at 14:01

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