Timeline for Methods for combining features other than concatenation
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 19, 2023 at 9:10 | comment | added | Luca Anzalone | In general, when you want to combine two feature sets you want to also combine (and so preserve) their respective information. I guess introducing random kernels is a bad idea, maybe the average is a little better. Anyway, in the paper section D (page 4) they propose 2 strategies: addition and channel strategy. Have you looked at the channel strategy? Maybe you can combine (or weight) the RGB features with the pooled ones of the multispectral input | |
| Apr 18, 2023 at 20:26 | comment | added | programmer_04_03 | And I was also thinking about some convolution operations with predefined kernels such as Random Gaussian or average value. Do you think that will be helpful? | |
| Apr 18, 2023 at 20:16 | comment | added | programmer_04_03 | Anyalone Thanks for the inputs. I think I will stay away from parameters that I will have to further learn in the model. Because the goal is to achieve min possible training time. So, less the parameters, the better. | |
| Apr 18, 2023 at 20:01 | history | answered | Luca Anzalone | CC BY-SA 4.0 |