Artifacts After pruning Unet CNN I'm trying to make a dark image brighter using CNN-UNet architecture.
When I train the network, I get the following results:

When I cut the features in half for pruning, and do full train again, I get the following result:

There are those bubble artifacts near the light.
Why are they happening?

How can I locate the problematic neurons that cause it?

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