Regarding Loss Function using ground truth labels in linear_loss part

[SonalKumar95]

Hello there,

Please let me know if I'm wrong. In the train_segmentation.py, the loss function includes two extra loss functions, i.e., linear_loss and cluster_loss. For unsupervised training, the cluster_loss seems okay, but the linear_loss uses the ground truth labels. Is it the case or I'm getting it wrong? Please help me.

Thanks in advance.

[mhamilton723]

Hey Sonal, thats okay because theres a detach and clone before the linear loss. Its basically just an entirely separate probe component whose gradients dont alter the training of the unsupervised components.

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On Wed, Jan 10, 2024 at 1:42 AM Sonal Kumar ***@***.***> wrote: Hello there, Please let me know if I'm wrong. In the train_segmentation.py, the loss function includes two extra loss functions, i.e., linear_loss and cluster_loss. For unsupervised training, the cluster_loss seems okay, but the linear_loss uses the ground truth labels. Is it the case or I'm getting it wrong? Please help me. Thanks in advance. — Reply to this email directly, view it on GitHub <#89> or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABRIKPPBI2BLGJEU3TT4EYTYNYZ4RBFKMF2HI4TJMJ2XIZLTSOBKK5TBNR2WLJDUOJ2WLJDOMFWWLO3UNBZGKYLEL5YGC4TUNFRWS4DBNZ2F6YLDORUXM2LUPGBKK5TBNR2WLJLJONZXKZNENZQW2ZNLORUHEZLBMRPXI6LQMWBKK5TBNR2WLJDUOJ2WLJDOMFWWLLTXMF2GG2C7MFRXI2LWNF2HTLDTOVRGUZLDORPXI6LQMWSUS43TOVS2M5DPOBUWG44SQKSHI6LQMWVHEZLQN5ZWS5DPOJ42K5TBNR2WLKJUGY3TCNBSHEZDLAVEOR4XAZNFNFZXG5LFUV3GC3DVMWVDEMBXGM3TAMZRGAYKO5DSNFTWOZLSUZRXEZLBORSQ> . You are receiving this email because you are subscribed to this thread. Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub> .

[bio-mlhui]

I am also confused. The detached_code = torch.clone(model_output[1].detach()) will generate detached_code which does not requires grad. However, the linear_output = self.linear_model(detached_code) will generate linear_output which requires grad. Since linear_output is used to compute linear_loss which uses ground truth mask labels, the final loss will backward its gradient to the linear_model. Does this mean the final algorithm is not unsupervised?