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Regularizers

Regularizers are applied to weights and embeddings without the need for labels or tuples.

Here is an example of a weight regularizer being passed to a loss function.

from pytorch_metric_learning import losses, regularizers
R = regularizers.RegularFaceRegularizer()
loss = losses.ArcFaceLoss(margin=30, num_classes=100, embedding_size=128, weight_regularizer=R)

BaseRegularizer

regularizers.BaseWeightRegularizer(collect_stats = True, 
                                reducer = None, 
                                distance = None)

An object that extends this class can be passed as the embedding_regularizer into any loss function. It can also be passed as the weight_regularizer into any class that extends WeightRegularizerMixin.

Parameters

  • collect_stats: If True, will collect various statistics that may be useful to analyze during experiments. If False, these computations will be skipped.
  • reducer: A reducer object. If None, then the default reducer will be used.
  • distance: A distance object. If None, then the default distance will be used.

Default distance:

Default reducer:

CenterInvariantRegularizer

Deep Face Recognition with Center Invariant Loss

This encourages unnormalized embeddings or weights to all have the same Lp norm.

regularizers.CenterInvariantRegularizer(**kwargs)

Default distance:

Default reducer:

LpRegularizer

This encourages embeddings/weights to have a small Lp norm.

regularizers.LpRegularizer(p=2, **kwargs)

Parameters

  • p: The type of norm. For example, p=1 is the Manhattan distance, and p=2 is Euclidean distance.

Default distance:

  • This regularizer does not use a distance object, so setting this parameter will have no effect.

Default reducer:

RegularFaceRegularizer

RegularFace: Deep Face Recognition via Exclusive Regularization

This should be applied as a weight regularizer. It penalizes class vectors that are very close together.

regularizers.RegularFaceRegularizer(**kwargs)

Default distance:

Default reducer:

SparseCentersRegularizer

SoftTriple Loss: Deep Metric Learning Without Triplet Sampling

This should be applied as a weight regularizer. It encourages multiple class centers to "merge", i.e. group together.

regularizers.SparseCentersRegularizer(num_classes, centers_per_class, **kwargs)

Parameters

  • num_classes: The number of classes in your training dataset.
  • centers_per_class: The number of rows in the weight matrix that correspond to 1 class.

Default distance:

Default reducer:

ZeroMeanRegularizer

Signal-to-Noise Ratio: A Robust Distance Metric for Deep Metric Learning

regularizers.ZeroMeanRegularizer(num_classes, centers_per_class, **kwargs)

Equation

In this equation, N is the batch size, M is the size of each embedding.

zero_mean_regularizer_equation

Default distance:

  • This regularizer does not use a distance object, so setting this parameter will have no effect.

Default reducer: