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Adamdad avatar Adamdad commented on July 20, 2024 2

Dear @intelyoungway, thank you so much for the questions. Yes, our framework support arbitrary network reassembly across tasks and datasets. However, that would be out of the scope of a single paper.

For your Scenario 1, I would guess the reassembled model (R101+FRCNN-R101+DeepLabv3-R101) would be better at certain dense prediction tasks. And for your Scenario 2, I guess if dataset 1 is somehow distorted or biased (e.g. limited label size/long tail pattern/noisy labels), borrowing the knowledge from other datasets 2/3 might help the performance on dataset 1.

Since our reassebly framework is quite general, those scenarios would be interesting extension of our study. Best.

from dery.

intelyoungway avatar intelyoungway commented on July 20, 2024 1

Dear @intelyoungway, thank you so much for the questions. Yes, our framework support arbitrary network reassembly across tasks and datasets. However, that would be out of the scope of a single paper.

For your Scenario 1, I would guess the reassembled model (R101+FRCNN-R101+DeepLabv3-R101) would be better at certain dense prediction tasks. And for your Scenario 2, I guess if dataset 1 is somehow distorted or biased (e.g. limited label size/long tail pattern/noisy labels), borrowing the knowledge from other datasets 2/3 might help the performance on dataset 1.

Since our reassebly framework is quite general, those scenarios would be interesting extension of our study. Best.

Thanks for your quick reply! That is really useful for me! Again, hope more people could know and use DeRy!

from dery.

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