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Support for customized optimizers during training allows for greater flexibility in training models. Some frameworks in WSI tasks consist of multiple networks. Right now, we can only build a model with a single network.
Pitch
There are some models that require customized optimizers, typically models that are consisted of several modules. For example, WENO is consisted of a student network and a teacher network. Right now, I cannot implement WENO using SlideFlow.
Alternatives
I don't think the current version has support for such knowledge distillation network.
Additional context
The text was updated successfully, but these errors were encountered:
Thanks for this suggestion. At present, Slideflow could be used for image processing and preparation of feature bags (in PyTorch format) for external, customized MIL training loops like WENO. I will look into whether we can extend our MIL training framework to add support for custom optimizers. I don't think it would take too much work.
Feature
Support for customized optimizers during training allows for greater flexibility in training models. Some frameworks in WSI tasks consist of multiple networks. Right now, we can only build a model with a single network.
Pitch
There are some models that require customized optimizers, typically models that are consisted of several modules. For example, WENO is consisted of a student network and a teacher network. Right now, I cannot implement WENO using SlideFlow.
Alternatives
I don't think the current version has support for such knowledge distillation network.
Additional context
The text was updated successfully, but these errors were encountered: