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Source code for tllib.regularization.bss

"""
@author: Yifei Ji
@contact: jiyf990330@163.com
"""
import torch
import torch.nn as nn

__all__ = ['BatchSpectralShrinkage']


[docs]class BatchSpectralShrinkage(nn.Module): r""" The regularization term in `Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning (NIPS 2019) <https://proceedings.neurips.cc/paper/2019/file/c6bff625bdb0393992c9d4db0c6bbe45-Paper.pdf>`_. The BSS regularization of feature matrix :math:`F` can be described as: .. math:: L_{bss}(F) = \sum_{i=1}^{k} \sigma_{-i}^2 , where :math:`k` is the number of singular values to be penalized, :math:`\sigma_{-i}` is the :math:`i`-th smallest singular value of feature matrix :math:`F`. All the singular values of feature matrix :math:`F` are computed by `SVD`: .. math:: F = U\Sigma V^T, where the main diagonal elements of the singular value matrix :math:`\Sigma` is :math:`[\sigma_1, \sigma_2, ..., \sigma_b]`. Args: k (int): The number of singular values to be penalized. Default: 1 Shape: - Input: :math:`(b, |\mathcal{f}|)` where :math:`b` is the batch size and :math:`|\mathcal{f}|` is feature dimension. - Output: scalar. """ def __init__(self, k=1): super(BatchSpectralShrinkage, self).__init__() self.k = k def forward(self, feature): result = 0 u, s, v = torch.svd(feature.t()) num = s.size(0) for i in range(self.k): result += torch.pow(s[num-1-i], 2) return result

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