regarding hierarchical representation pattern about asrf_with_asformer HOT 4 CLOSED

• windows of 512 is worse than 2**i in our experiments.
• Yes. sliding window approach is slightly better than block-wise approach.
• The current setting is what I used. The reason that the performance is possibly due to the unstable training process in ASRF due to the boundary prediction. I strongly recommend you to pick the best model according to the validation set instead of directly use the model from 80 epoch. By the way, if you want to get out of the trivial param search, I recommend you to use the pure ASformer in https://github.com/ChinaYi/ASFormer , where the training process of our pure ASFormer is very stable and not sensitive to the training epochs.

from asrf_with_asformer.

In libs/models/tcn.py(L472-L473),

self.layers = nn.ModuleList(
            [AttModule(2 ** i, num_f_maps, num_f_maps, r1, r2, att_type, 'encoder', alpha) for i in # 2**i
             range(num_layers)])

where num_layers=10, which means that the last attention module has the windows of 512. If you want to reproduce the ablation study, just replace the 2**i with 512, so that each layer will have windows of 512.

from asrf_with_asformer.

@ChinaYi thank you for your answer,

• should windows of 512 works better or worse than 2**i in your opinion?

• Also one more thing, should I use sliding attention option to achieve the best results in the paper?

• finally, can you please let me know the parameters that is used to achieve the best performance for encoder and decoder. i mean te parameters that can lead to the best results in the paper. I notice the performance drops slightly when I go with the current default settings

from asrf_with_asformer.

thanks for the hints! appreciate it!

from asrf_with_asformer.