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The partition-based formulation currently only works for dense neural networks. We would like to extend the partition-based formulation to additionally apply to CNNs.
Even better: generalize the partition-based formulation for both fully-dense NNs and CNNs and thereby avoid having the same tricky indexing in two places.
This discussion was converted from issue #54 on February 15, 2022 11:49.
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The partition-based formulation currently only works for dense neural networks. We would like to extend the partition-based formulation to additionally apply to CNNs.
A research paper describing the partition-based formulation is here: https://proceedings.neurips.cc/paper/2021/hash/17f98ddf040204eda0af36a108cbdea4-Abstract.html
The challenge is to write code equivalent to partition_based.py but with the same careful indexing that had to happen here:
OMLT/src/omlt/neuralnet/layer.py
Line 171 in 683caa7
Even better: generalize the partition-based formulation for both fully-dense NNs and CNNs and thereby avoid having the same tricky indexing in two places.
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