skada.deep.SPA

skada.deep.SPA(module, layer_name, reg_adv=1, reg_gsa=1, reg_nap=1, domain_classifier=None, num_features=None, base_criterion=None, domain_criterion=None, callbacks=None, max_epochs=100, **kwargs)

Domain Adaptation with SPA.

From [36].

Parameters:

moduletorch module (class or instance)

A PyTorch Module. In general, the uninstantiated class should be passed, although instantiated modules will also work.

layer_namestr

The name of the module's layer whose outputs are collected during the training.

regfloat, default=1

Regularization parameter for DA loss.

domain_classifiertorch module, default=None

A PyTorch Module used to classify the domain. If None, a domain classifier is created following [1]_.

num_featuresint, default=None

Size of the input of domain classifier, e.g size of the last layer of the feature extractor. If domain_classifier is None, num_features has to be provided.

base_criteriontorch criterion (class)

The base criterion used to compute the loss with source labels. If None, the default is torch.nn.CrossEntropyLoss.

domain_criteriontorch criterion (class)

The criterion (loss) used to compute the adversarial loss. If None, a BCELoss is used.

callbackslist, default=None

List of callbacks to use during training.

max_epochsint, default=100

Maximum number of epochs to train the model.

References

[36]

Xiao et. al. SPA: A Graph Spectral Alignment Perspective for Domain Adaptation. In Neurips, 2023.