How to use pre-trained weights to initialize the custom CNN?
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From this paperhere, it shows that U_Net initialized by VGG received a better result than the one trained from scratch. Now I want to build a custom u_net which has [32,64,128,256] which is different from vgg.features (I have tried to use U_Net with VGG architecture without pre-trained weights and custom U_Net, they all get the same Iou score .This means that the sample architecture suits for my data. ) How can I get the initialized weight from VGG? As far as I know, knowledge distillation could help me to get a simple net whose weight could be used for initialization for my u-net like ([32,64,128,256]). But knowledge distillation on ImageNet data set would cost lost of time, Is there another way to initialize the ‘smaller’ U-Net([32,64,128,256] )?
deep-learning cnn
asked 22 hours ago
user66596user66596
133
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add a comment |
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From this paperhere, it shows that U_Net initialized by VGG received a better result than the one trained from scratch. Now I want to build a custom u_net which has [32,64,128,256] which is different from vgg.features (I have tried to use U_Net with VGG architecture without pre-trained weights and custom U_Net, they all get the same Iou score .This means that the sample architecture suits for my data. ) How can I get the initialized weight from VGG? As far as I know, knowledge distillation could help me to get a simple net whose weight could be used for initialization for my u-net like ([32,64,128,256]). But knowledge distillation on ImageNet data set would cost lost of time, Is there another way to initialize the ‘smaller’ U-Net([32,64,128,256] )?
deep-learning cnn
asked 22 hours ago
user66596user66596
133
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Here is the two paper I referred above, arxiv.org/abs/1801.05746 arxiv.org/abs/1503.02531
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– user66596
22 hours ago
add a comment |
$begingroup$
From this paperhere, it shows that U_Net initialized by VGG received a better result than the one trained from scratch. Now I want to build a custom u_net which has [32,64,128,256] which is different from vgg.features (I have tried to use U_Net with VGG architecture without pre-trained weights and custom U_Net, they all get the same Iou score .This means that the sample architecture suits for my data. ) How can I get the initialized weight from VGG? As far as I know, knowledge distillation could help me to get a simple net whose weight could be used for initialization for my u-net like ([32,64,128,256]). But knowledge distillation on ImageNet data set would cost lost of time, Is there another way to initialize the ‘smaller’ U-Net([32,64,128,256] )?
deep-learning cnn
asked 22 hours ago
user66596user66596
133
$endgroup$
From this paperhere, it shows that U_Net initialized by VGG received a better result than the one trained from scratch. Now I want to build a custom u_net which has [32,64,128,256] which is different from vgg.features (I have tried to use U_Net with VGG architecture without pre-trained weights and custom U_Net, they all get the same Iou score .This means that the sample architecture suits for my data. ) How can I get the initialized weight from VGG? As far as I know, knowledge distillation could help me to get a simple net whose weight could be used for initialization for my u-net like ([32,64,128,256]). But knowledge distillation on ImageNet data set would cost lost of time, Is there another way to initialize the ‘smaller’ U-Net([32,64,128,256] )?
deep-learning cnn
deep-learning cnn
asked 22 hours ago
user66596user66596
133
asked 22 hours ago
user66596user66596
133
asked 22 hours ago
user66596user66596
133
asked 22 hours ago
user66596user66596
133
asked 22 hours ago
user66596user66596
133
133
$begingroup$
Here is the two paper I referred above, arxiv.org/abs/1801.05746 arxiv.org/abs/1503.02531
$endgroup$
– user66596
22 hours ago
add a comment |
$begingroup$
Here is the two paper I referred above, arxiv.org/abs/1801.05746 arxiv.org/abs/1503.02531
$endgroup$
– user66596
22 hours ago
$begingroup$
Here is the two paper I referred above, arxiv.org/abs/1801.05746 arxiv.org/abs/1503.02531
$endgroup$
– user66596
22 hours ago
$begingroup$
Here is the two paper I referred above, arxiv.org/abs/1801.05746 arxiv.org/abs/1503.02531
$endgroup$
– user66596
22 hours ago
add a comment |
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Here is the two paper I referred above, arxiv.org/abs/1801.05746 arxiv.org/abs/1503.02531
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– user66596
22 hours ago