Thanks again :-) Loved the critic at the end. Also, nice from them that they report these results, lots of papers would silence it to make it seem like the method brought all the gains !
Yannic, nice coverage of the paper. I have one question: at 15:05, you explain that the pseudo-label is used only if the confidence is above a certain threshold (which is also a hyperparameter). Where is the confidence coming from? It is well known that the confidence score coming out of softmax is not reliable. Can you please explain?
Something which I find weird: isn’t a constant pseudolabel always correct? It seems that there are only positive examples in the scheme which uses the unlabeled data, and so there is nothing in the loss which forces the model to not always output the same pseudolabel for everything. Yes, one can argue that this would fail the supervised loss, but then the question becomes “how is the supervised loss weighted w.r.t. the unsupervised loss”. In any case, it seems that one would also desire to have negative examples in the unsupervised case
In what order do they train the model, feed the labelled image first and then the unlabelled ones? Also, can two unlabelled images of the same class have a different pseudo label?
I think they do everything at the same time. I guess the labelled images can also go the unlabelled way, yes. But not the other way around, obviously :)
Thanks again :-)
Loved the critic at the end.
Also, nice from them that they report these results, lots of papers would silence it to make it seem like the method brought all the gains !
78% accuracy from 1 image per class. This blew my mind.
What a time to be alive.
HOLD ON TO YOUR PAPERS
my brain matter is all over the floor right now hhh
@@TeoZarkopafilis Woah! A fellow scholar here!
definitely one of the best ways to explain a paper!! Kudos to you
I loved the critique at the end. Thanks.
just like UDA, emphasis on way you augment. and poor UDA got rejected. ps LOVE your breakdowns, please keep them coming.
well explained. Thank you
Thanks again...Great teacher for us students. 🙂
Really nice explanation and concise...
Fantastic!
Yannic, nice coverage of the paper. I have one question: at 15:05, you explain that the pseudo-label is used only if the confidence is above a certain threshold (which is also a hyperparameter). Where is the confidence coming from? It is well known that the confidence score coming out of softmax is not reliable. Can you please explain?
I love this content :D
Really Good Job please keep going
Nice edit
Something which I find weird: isn’t a constant pseudolabel always correct? It seems that there are only positive examples in the scheme which uses the unlabeled data, and so there is nothing in the loss which forces the model to not always output the same pseudolabel for everything.
Yes, one can argue that this would fail the supervised loss, but then the question becomes “how is the supervised loss weighted w.r.t. the unsupervised loss”. In any case, it seems that one would also desire to have negative examples in the unsupervised case
In what order do they train the model, feed the labelled image first and then the unlabelled ones? Also, can two unlabelled images of the same class have a different pseudo label?
I think they do everything at the same time. I guess the labelled images can also go the unlabelled way, yes. But not the other way around, obviously :)
What is the software that you are using to annotate papers Yannic ? I am using Margin notes but it does not seem as smooth
Google again, wow! 😂