Unofficial implementation of ManifoldMixup (Proceedings of ICML 19) for fast.ai (V2) based on Shivam Saboo's pytorch implementation of manifold mixup, fastai's input mixup implementation plus some improvements/variants that I developped with lessw2020.

This package provides four additional callbacks to the fastai learner :

• ManifoldMixup which implements ManifoldMixup
• OutputMixup which implements a variant that does the mixup only on the output of the last layer (this was shown to be more performant on a benchmark and an independant blogpost)
• DynamicManifoldMixup which lets you use manifold mixup with a schedule to increase difficulty progressively
• DynamicOutputMixup which lets you use manifold mixup with a schedule to increase difficulty progressively

For a minimal demonstration of the various callbacks and their parameters, see the Demo notebook.

To use manifold mixup, you need to import manifold_mixup and pass the corresponding callback to the cbs argument of your learner :

learner = Learner(data, model, cbs=ManifoldMixup())
learner.fit(8)

The ManifoldMixup callback takes three parameters :

• alpha=0.4 parameter of the beta law used to sample the interpolation weight
• use_input_mixup=True do you want to apply mixup to the inputs
• module_list=None can be used to pass an explicit list of target modules

The OutputMixup variant takes only the alpha parameters.

Dynamic callbackss, which are available via dynamic_mixup, take three parameters instead of the single alpha parameter :

• alpha_min=0.0 the initial, minimum, value for the parameter of the beta law used to sample the interpolation weight (we recommend keeping it to 0)
• alpha_max=0.6 the final, maximum, value for the parameter of the beta law used to sample the interpolation weight
• scheduler=SchedCos the scheduling function to describe the evolution of alpha from alpha_min to alpha_max

The default schedulers are SchedLin, SchedCos, SchedNo, SchedExp and SchedPoly. See the Annealing section of fastai2's documentation for more informations on available schedulers, ways to combine them and provide your own.

ManifoldMixup tries to establish a sensible list of modules on which to apply mixup:

• it uses a user provided module_list if possible
• otherwise it uses only the modules wrapped with ManifoldMixupModule
• if none are found, it defaults to modules with Block or Bottleneck in their name (targetting mostly resblocks)
• finaly, if needed, it defaults to all modules that are not included in the non_mixable_module_types list

The non_mixable_module_types list contains mostly recurrent layers but you can add elements to it in order to define module classes that should not be used for mixup (do not hesitate to create an issue or start a PR to add common modules to the default list).

OutputMixup applies the mixup directly to the output of the last layer. This only works if the loss function contains something like a softmax (and not when it is directly used as it is for regression).

Thus, OutputMixup cannot be used for regression.

ManifoldMixup (this does not apply to OutputMixup) is greatly degraded when applied inside a residual block. This is due to the mixed-up values becoming incoherent with the output of the skip connection (which have not been mixed).

While this implementation is equiped to work around the problem for U-Net and ResNet like architectures, you might run into problems (negligeable improvements over the baseline) with other network structures. In which case, the best way to apply manifold mixup would be to manually select the modules to be instrumented.

For more unofficial fastai extensions, see the Fastai Extensions Repository.