gigl.src.common.models.layers.loss

Classes

AligmentLoss	Taken from THUwangcy/DirectAU, AlignmentLoss increases the similarity of representations between positive user-item pairs.
BGRLLoss	Leverages BGRL loss from https://arxiv.org/pdf/2102.06514.pdf, using an offline and online encoder to predict alternative augmentations of the input.
FeatureReconstructionLoss	Computes SCE between original feature and reconstructed feature.
GBTLoss	Computes the Barlow Twins loss on the two input matrices as an auxiliary loss.
GRACELoss	A loss class that implements the GRACE (https://arxiv.org/pdf/2006.04131.pdf) contrastive loss approach.
KLLoss	Calculates KL Divergence between two set of scores for the distribution loss.
LLPRankingLoss	Calculates a margin-based rakning loss between two set of scores for the ranking loss in LLP.
MarginLoss	A loss layer built on top of the PyTorch implementation of the margin ranking loss.
ModelResultType	An enumeration.
RetrievalLoss	A loss layer built on top of the tensorflow_recommenders implementation.
SoftmaxLoss	A loss layer built on top of the PyTorch implementation of the softmax cross entropy loss.
TBGRLLoss	TBGRL (https://arxiv.org/pdf/2211.14394.pdf) improves over BGRL by generating a third augmented graph as a negative sample, providing a cheap corruption that improves generalizability of the model in inductive settings.
UniformityLoss	Taken from THUwangcy/DirectAU, UniformityLoss measures how well the representations scatter on the hypersphere.
WhiteningDecorrelationLoss	Utilizes canonical correlation analysis to compute similarity between augmented graphs as an auxiliary loss.