Comments (6)
Using the dataset for scientific research requires signing an agreement. Please email the points of contact mentioned in the Appendix of the original paper:
from deep-multimodal-subspace-clustering-networks.
Using the dataset for scientific research requires signing an agreement. Please email the points of contact mentioned in the Appendix of the original paper:
Thank!I aslo want to know the input size of minst?
from deep-multimodal-subspace-clustering-networks.
Using the dataset for scientific research requires signing an agreement. Please email the points of contact mentioned in the Appendix of the original paper:
Also,when I scale tihe input pixel into 0-1(pixel/255),the result is so bad,why?
from deep-multimodal-subspace-clustering-networks.
The input size for MNIST dataset is also 32x32. (pixel's range between 0 and 255).
If you normalize your data to a range between 0 and 1, you may need to find appropriate regularization parameters through cross-validation (reg_constant1 and reg_constant2 in the code.). The current regularization parameters are borrowed from the DSC paper, where their input is ranged between 0 and 255.
Please check the data processing section in the readme.
from deep-multimodal-subspace-clustering-networks.
The input size for MNIST dataset is also 32x32. (pixel's range between 0 and 255).
If you normalize your data to a range between 0 and 1, you may need to find appropriate regularization parameters through cross-validation (reg_constant1 and reg_constant2 in the code.). The current regularization parameters are borrowed from the DSC paper, where their input is ranged between 0 and 255.
Please check the data processing section in the readme.
Thank!Also, Coulg you provide the code for plot the picture :
I did not find the function in python?
from deep-multimodal-subspace-clustering-networks.
I train on the digit dataset,the result is bad:
the result decreases as the epoch growing,why?
I have checked the dataset and the network,and the vision of restruction picture is good
from deep-multimodal-subspace-clustering-networks.
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