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Seemingly, there is three training stage according to your paper:
Stage 1: training residual extraction network only
Stage 2: training density classification network based on residue output from the network in Stage 1
Stage 3: joint training of both networks
quote: "To efficiently train the classifier, a two-stage training protocol is leveraged. A residual feature extraction network is firstly trained to estimate the residual part of the given rainy image, then a classification sub-network is trained using the estimated residual as the input and is optimized via the ground truth labels (rain-density). Finally, the two stages (feature extraction and classification) are jointly optimized."
It seems that the code of Stage 1 corresponds to file derain_residual.py, and Stage 3 corresponds to file train_rain_class.py (which directly loads two pre-trained models). How to get the density classifier trained in Stage 2?
The text was updated successfully, but these errors were encountered:
Seemingly, there is three training stage according to your paper:
It seems that the code of Stage 1 corresponds to file
derain_residual.py
, and Stage 3 corresponds to filetrain_rain_class.py
(which directly loads two pre-trained models). How to get the density classifier trained in Stage 2?The text was updated successfully, but these errors were encountered: