Note that it cites TabDDPM in the related work, but that is for diffusione on tabular data! While most tabular data is low-dimensional, the type of low-dimensional data tackled in the paper is not tabular!
I'm also not quite sure how the linear upscaling is supposed to help, as it can be absorbed into the first layer of the following MLP, so I would rather think that the performance improvement (if any, the numbers are quite close and lack standard errors) is either due to the increased number of trainable parameters or some kind of ensembling effect (essentially the mixture of experts point made by the human authors).
Note that it cites TabDDPM in the related work, but that is for diffusione on tabular data! While most tabular data is low-dimensional, the type of low-dimensional data tackled in the paper is not tabular!
I'm also not quite sure how the linear upscaling is supposed to help, as it can be absorbed into the first layer of the following MLP, so I would rather think that the performance improvement (if any, the numbers are quite close and lack standard errors) is either due to the increased number of trainable parameters or some kind of ensembling effect (essentially the mixture of experts point made by the human authors).