search

wyf0912 / LDDG

[NIPS 2020] The code release of paper 'Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization'
61 stars 9 forks source link

Paper problem #1

Closed dhx000 closed 3 years ago

dhx000 commented 3 years ago

I have a question about your paper,The last third line on Page 3:‘’in other words,the rank of matirx is expected to be 1"?

wyf0912 commented 3 years ago

Hi, sorry for the confusion . In fact, it is a typo. In camera-ready version, we have corrected it. I will also update the arxiv version soon.

In other words, there exists a dominant eigenvalue capturing the category information of the matrix . Therefore, given a sample mini-batch denoted by $\mathcal{X} = {x_i^k}$, we can obtain the corresponding latent features as $\mathcal{Z}$ through a posterior $q(z|x)$ parameterized by an encoder. By further conducting mode-1 flattening $\mathcal{Z}$ as $\mathbf{Z}$\footnote{We assume that the first dimension is associated with sample index.}, our proposed rank regularization can be given as $rank(\mathbf{Z}) = C$, where $C$ is the number of categories of a specific task.

dhx000 commented 3 years ago

Thank you very much.

wyf0912 commented 3 years ago

HI, the version in arxiv has updated