  Converting deep learning models between frameworks is a common step to
maximize model compatibility across devices and leverage optimization features
that may be exclusively provided in one deep learning framework. However, this
conversion process may be riddled with bugs, making the converted models either
undeployable or problematic, considerably degrading their prediction
correctness.
  In this paper we propose an automated approach for fault localization and
repair, Fix-Con, during model conversion between deep learning frameworks.
Fix-Con is capable of detecting and fixing faults introduced in model input,
parameters, hyperparameters, and the model graph during conversion.
  Fix-Con uses a set of fault types (mined from surveying conversion issues
reported \nick{in code repositories and forums}) to localize potential
conversion faults in the converted target model and then repair them
appropriately, e.g., replacing the parameters of the target model with those
from the source model. This is done iteratively for every image in the dataset,
comparing output label differences between the source model and the converted
target model until all differences are resolved. We evaluate the effectiveness
of Fix-Con in fixing model conversion bugs of three widely used image
recognition models converted across four different deep learning frameworks.
Overall, Fix-Con was able to fix $462$ out of $755$ detected conversion faults,
either completely repairing or significantly improving the performance of $14$
out of the $15$ erroneous conversion cases.
