About how to achieve L_m Loss on SWAV about un-mix HOT 3 CLOSED

Hi @Simon-Stma

Thanks for your interest in our work. It's easy to generalize UnMix to clustering-based methods like SwAV. You can simply consider replacing the soft distance of positive and negative pairs in contrastive loss with soft cluster assignment (i.e., soft fitness between features z and a code q) in SwAV. Specifically, in SwAV, there are similarly two image features $\mathbf z_t, \ \mathbf z_s$ from two different augmentations of the same image, the loss function of SwAV is:
$L_{ori}(\mathbf{z}_t, \mathbf{z}_s)=\ell(\mathbf{z}_t, \mathbf{q}_s)+\ell(\mathbf{z}_s, \mathbf{q}_t)$

where $\ell(\mathbf{z}_t, \mathbf{q}_s)=-\sum_k \mathbf{q}_s^{(k)} \log \mathbf{p}_t^{(k)}$

For UnMix on SwAV, we can also replace one of image feature with normal order of mixture and reverse order of mixture, i.e., $\mathbf z_t^m, \mathbf z^{rm}_t$ , then $L_M$ will be:
$L_M=\lambda L_M(\mathbf{z}^m_t, \mathbf{z}_s)+(1-\lambda)L_M(\mathbf{z}^{rm}_t, \mathbf{z}_s)=\lambda(\ell(\mathbf{z}^m_t, \mathbf{q}_s)+\ell(\mathbf{z}_s, \mathbf{q}^m_t))+(1-\lambda)(\ell(\mathbf{z}^{rm}_t, \mathbf{q}_s)+\ell(\mathbf{z}_s, \mathbf{q}^{rm}_t))$

The final objective is:
$L_{all}=L_{ori}(\mathbf{z}_t, \mathbf{z}_s)+\lambda L_M(\mathbf{z}^m_t, \mathbf{z}_s)+(1-\lambda)L_M(\mathbf{z}^{rm}_t, \mathbf{z}_s)$

from un-mix.

Thank you very much for your reply in your busy schedule！ I understand what you mean。

from un-mix.

Hi @Simon-Stma We have released the code of Un-Mix + SwAV on CIFAR and ImageNet datasets: https://github.com/szq0214/Un-Mix/tree/master/UnMix_SwAV, you can have a look in this repo for the implementation.

from un-mix.