Gb/lr loss

[grantbuster]

@bnb32 your success with the ERA wind model had me thinking about allowing the model more freedom to create it's own realistic high-res fields, but with the climate change applications i still want to adhere pretty strictly to the bias corrected climatology. This low-res loss function was something Ryan or Malik had used in prior work (i think the diversity gan?) and i think it makes a lot of sense given the reasoning above. I also added in an extremes feature to the lr loss but honestly initial tests are showing the lr loss alone having very good dynamic range.

[bnb32]

@bnb32 your success with the ERA wind model had me thinking about allowing the model more freedom to create it's own realistic high-res fields, but with the climate change applications i still want to adhere pretty strictly to the bias corrected climatology. This low-res loss function was something Ryan or Malik had used in prior work (i think the diversity gan?) and i think it makes a lot of sense given the reasoning above. I also added in an extremes feature to the lr loss but honestly initial tests are showing the lr loss alone having very good dynamic range.

This is an interesting idea. Do you have a link to the paper? I'm curious what stops the generator from just repeating pixels in the high res so that the coarse version agrees exactly with the low res.

EDIT: oh, nvm. I see that you're still using the high res and synthetic. I thought you were coarsening the synthetic and computing loss with the lr input.

[bnb32]

good addition :)

[grantbuster]

Haha yeah I cant believe we didnt have an explicit test for this previously! i was so nervous that this test would fail!

[bnb32]

lmao that would have been nuts

[grantbuster]

@bnb32 your success with the ERA wind model had me thinking about allowing the model more freedom to create it's own realistic high-res fields, but with the climate change applications i still want to adhere pretty strictly to the bias corrected climatology. This low-res loss function was something Ryan or Malik had used in prior work (i think the diversity gan?) and i think it makes a lot of sense given the reasoning above. I also added in an extremes feature to the lr loss but honestly initial tests are showing the lr loss alone having very good dynamic range.

This is an interesting idea. Do you have a link to the paper? I'm curious what stops the generator from just repeating pixels in the high res so that the coarse version agrees exactly with the low res.

EDIT: oh, nvm. I see that you're still using the high res and synthetic. I thought you were coarsening the synthetic and computing loss with the lr input.

I dont have a paper i just remember a conversation from a year or two ago. Well we're using high-res and synthetic but yes we ARE coarsening synthetic and high-res and computing loss on the low res fields (basically the same as coarsened synthetic and lr input). The discriminator is what stops the generator from making a high-res blocky mess that agrees with low res.

[bnb32]

@bnb32 your success with the ERA wind model had me thinking about allowing the model more freedom to create it's own realistic high-res fields, but with the climate change applications i still want to adhere pretty strictly to the bias corrected climatology. This low-res loss function was something Ryan or Malik had used in prior work (i think the diversity gan?) and i think it makes a lot of sense given the reasoning above. I also added in an extremes feature to the lr loss but honestly initial tests are showing the lr loss alone having very good dynamic range.

This is an interesting idea. Do you have a link to the paper? I'm curious what stops the generator from just repeating pixels in the high res so that the coarse version agrees exactly with the low res.
EDIT: oh, nvm. I see that you're still using the high res and synthetic. I thought you were coarsening the synthetic and computing loss with the lr input.

I dont have a paper i just remember a conversation from a year or two ago. Well we're using high-res and synthetic but yes we ARE coarsening synthetic and high-res and computing loss on the low res fields (basically the same as coarsened synthetic and lr input). The discriminator is what stops the generator from making a high-res blocky mess that agrees with low res.

right, the disc still sees high res! I was thinking that we could get away with just storing coarsened hr with this loss function but i forgot about good old disc.

[bnb32]

looks like you have a leftover here @grantbuster

[grantbuster]

dang and just as the tests were almost done! haha no worries, i'll fix

[malihass]

@bnb32 your success with the ERA wind model had me thinking about allowing the model more freedom to create it's own realistic high-res fields, but with the climate change applications i still want to adhere pretty strictly to the bias corrected climatology. This low-res loss function was something Ryan or Malik had used in prior work (i think the diversity gan?) and i think it makes a lot of sense given the reasoning above. I also added in an extremes feature to the lr loss but honestly initial tests are showing the lr loss alone having very good dynamic range.

This is an interesting idea. Do you have a link to the paper? I'm curious what stops the generator from just repeating pixels in the high res so that the coarse version agrees exactly with the low res.
EDIT: oh, nvm. I see that you're still using the high res and synthetic. I thought you were coarsening the synthetic and computing loss with the lr input.

I dont have a paper i just remember a conversation from a year or two ago. Well we're using high-res and synthetic but yes we ARE coarsening synthetic and high-res and computing loss on the low res fields (basically the same as coarsened synthetic and lr input). The discriminator is what stops the generator from making a high-res blocky mess that agrees with low res.

right, the disc still sees high res! I was thinking that we could get away with just storing coarsened hr with this loss function but i forgot about good old disc.

I'm being a fly on the wall here!

@grantbuster @bnb32 You are talking about this ref? https://arxiv.org/pdf/2111.05962.pdf (maybe Eq 14?)
Doing the content loss on the low res field (instead of high res) forces to adhere to the low-res observation and gives freedom to the generator at the high res level. The idea was that the gen sticks to the little observation available, and is free to create features that the discriminator likes at the high-res level. So this strategy promotes diversity of the high-res features given the same low res data. The downside is that , as you immediately noted, you need to balance adversarial and content loss well, otherwise you end up with the high-res blocky problem.

If you do the content loss on the high-res data, then there is no high-res blocky issue, but you might kill the diversity if you try to generate many different high res fields. The upshot is stability and probably higher quality generated fields.

I think it is nice to have that content loss on low res data available now if we want to try some diverse field generation stuff!

[grantbuster]

@bnb32 your success with the ERA wind model had me thinking about allowing the model more freedom to create it's own realistic high-res fields, but with the climate change applications i still want to adhere pretty strictly to the bias corrected climatology. This low-res loss function was something Ryan or Malik had used in prior work (i think the diversity gan?) and i think it makes a lot of sense given the reasoning above. I also added in an extremes feature to the lr loss but honestly initial tests are showing the lr loss alone having very good dynamic range.

This is an interesting idea. Do you have a link to the paper? I'm curious what stops the generator from just repeating pixels in the high res so that the coarse version agrees exactly with the low res.
EDIT: oh, nvm. I see that you're still using the high res and synthetic. I thought you were coarsening the synthetic and computing loss with the lr input.

I dont have a paper i just remember a conversation from a year or two ago. Well we're using high-res and synthetic but yes we ARE coarsening synthetic and high-res and computing loss on the low res fields (basically the same as coarsened synthetic and lr input). The discriminator is what stops the generator from making a high-res blocky mess that agrees with low res.

right, the disc still sees high res! I was thinking that we could get away with just storing coarsened hr with this loss function but i forgot about good old disc.

I'm being a fly on the wall here!

@grantbuster @bnb32 You are talking about this ref? https://arxiv.org/pdf/2111.05962.pdf (maybe Eq 14?) Doing the content loss on the low res field (instead of high res) forces to adhere to the low-res observation and gives freedom to the generator at the high res level. The idea was that the gen sticks to the little observation available, and is free to create features that the discriminator likes at the high-res level. So this strategy promotes diversity of the high-res features given the same low res data. The downside is that , as you immediately noted, you need to balance adversarial and content loss well, otherwise you end up with the high-res blocky problem.

If you do the content loss on the high-res data, then there is no high-res blocky issue, but you might kill the diversity if you try to generate many different high res fields. The upshot is stability and probably higher quality generated fields.

I think it is nice to have that content loss on low res data available now if we want to try some diverse field generation stuff!

Yes exactly that! I think for the climate change applications this will be a good option to have because the GCM data may deviate from coarsened wtk/nsrdb so giving the generator more freedom to create the high-res fields may result in higher output quality overall