CAE
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lxtGH
Request for clarification on implementation
Hi, after reading your paper and studying the code, I don't understand why VisionTransformerForMaskedImageModeling have two implementations of the encoder (respectively encoder and teacher model). Why is it not possible to use just the encoder (since it seems they both have the same parameters)?
Hi, it is ok to use just the encoder. The so-called teacher model is actually the same as the encoder (structure/parameter).
Even for pre-training purposes? I'm trying to pre-train the model from scratch and I don't understand the utility of the teacher model. Why is not possible to do
with torch.no_grad(): latent_target = self.encoder(x, bool_masked_pos=(~bool_masked_pos))
instead of
with torch.no_grad(): latent_target = self.teacher(x, bool_masked_pos=(~bool_masked_pos))
It is ok to do so. This codebase defines a teacher model class to provide other researchers with the possibility to use EMA, although EMA is not used in CAE.
Hi, Sorry to bring up a one year old issue. However, I am unsure about an implementation detail.
https://github.com/lxtGH/CAE/blob/d72597143e486d9bbbaf1e3adc4fd9cfa618633a/models/modeling_cae.py#L114C58-L114C58
Here you update the teacher after computing the latent targets. Doesn't this mean that the target computation uses the teacher paramters of the previous step? Therefore the teacher and the encoder parameters are not synced. Is that on purpose?
