Let's say I was training a 4-D CNN (tesseract kernels). If my fourth dimension (temporal) is only 2 units, will having that extra dimension improve accuracy at all? Should I include more timepoints for my fourth dimension? If you don't understand the question, just ask, 4D CNNs are very rare and I just started researching it.

Comments

[deleted] t1_iuhu9aj wrote on October 31, 2022 at 1:26 PM

[removed]

suflaj t1_iuim5q2 wrote on October 31, 2022 at 4:42 PM

It could, but doesn't have to. For temporal dimensions 4 is very often seen, so you probably wanna start with that firat, then see how it compares to 3 or 2.

Intuitively, I think 2 time points are useless. It's difficult to generalize something new from such a short relation. Intuitively, I would like to sample t, t-1, t-2 and t-4, but I'd first confirm it's better than t, t-1, t-2 and t-3.

Rare_Lingonberry289 OP t1_iuisx3d wrote on October 31, 2022 at 5:27 PM

Do you think it would be beneficial to have 365 different time points (1 each day) in my fourth dimension? I know it’s hard to say, but what do you think?

suflaj t1_iuji61k wrote on October 31, 2022 at 8:15 PM

Probably not.

I am almost certain you don't have data that would take advantage of this dimensionality or the resources to process it
you can't accumulate so many features and remember all of them in recurrent models
I am almost certain you don't have the hardware to house such a large transformer model that could process it
I am almost certain you will not get a 365 day history of a sample during inference, 4 days seems more reasonable

Rare_Lingonberry289 OP t1_iujpxs1 wrote on October 31, 2022 at 9:09 PM

Ok so what if I have something like 10 temporal points, assuming you I have the necessary computing power. The other dimensions are pixels (x and y) and channels (bands).

suflaj t1_iuk2gfj wrote on October 31, 2022 at 10:43 PM

Yeah, just experiment with it. Like I said, I would start with 4. Then go higher or lower depending on your needs. I have personally not seen a temporally sensitive neural network to go beyond 6 or 8 time points. As with anything, there are tradeoffs.

Although if you have x, y and c, you will be doing 3D convolutions, not 4D. A 4D convolution on 4D data is essentially a linear layer.

Rare_Lingonberry289 OP t1_iuk3uxk wrote on October 31, 2022 at 10:54 PM

Ok, that makes sense. One more thing though. According to my research, temporal points during the spring and autumn are more helpful for what I'm trying to do. However, I'm afraid that large jumps like this will confuse my model. Like it will have a hard time detecting features when time jumps like this happen. Is this a real concern?

suflaj t1_iuk9d9w wrote on October 31, 2022 at 11:38 PM

As long as you keep the jumps the same it should be fine.