Combining 2 Neural Networks
$begingroup$
2 images as input, x1 and x2 and try to use convolution as a similarity measure. The idea is that the learned weights substitute more traditional measure of similarity (cross correlation, NN, ...). Defining my forward function as follows:
def forward(self,x1,x2):
out_conv1a = self.conv1(x1)
out_conv2a = self.conv2(out_conv1a)
out_conv3a = self.conv3(out_conv2a)
out_conv1b = self.conv1(x2)
out_conv2b = self.conv2(out_conv1b)
out_conv3b = self.conv3(out_conv2b)
Now for the similarity measure:
out_cat = torch.cat([out_conv3a, out_conv3b],dim=1)
futher_conv = nn.Conv2d(out_cat)
Question is as follows:
Would Depthwise/Separable Convolutions as in the google paper yield any advantage over 2d convolution of the concatenated input. For that matter can convolution be a similarity measure, cross correlation and convolution are very similar.
It is my understanding that the
groups=2option inconv2dwould provide 2 separate inputs to train weights with, in this case each of the previous networks weights. How are these combined afterwards?
python neural-network image-classification convolution pytorch
edited Oct 4 '18 at 0:29
timleathart
2,169726
asked Oct 3 '18 at 9:01
Benedict K.Benedict K.
1764
$endgroup$
bumped to the homepage by Community♦ 10 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
2 images as input, x1 and x2 and try to use convolution as a similarity measure. The idea is that the learned weights substitute more traditional measure of similarity (cross correlation, NN, ...). Defining my forward function as follows:
def forward(self,x1,x2):
out_conv1a = self.conv1(x1)
out_conv2a = self.conv2(out_conv1a)
out_conv3a = self.conv3(out_conv2a)
out_conv1b = self.conv1(x2)
out_conv2b = self.conv2(out_conv1b)
out_conv3b = self.conv3(out_conv2b)
Now for the similarity measure:
out_cat = torch.cat([out_conv3a, out_conv3b],dim=1)
futher_conv = nn.Conv2d(out_cat)
Question is as follows:
Would Depthwise/Separable Convolutions as in the google paper yield any advantage over 2d convolution of the concatenated input. For that matter can convolution be a similarity measure, cross correlation and convolution are very similar.
It is my understanding that the
groups=2option inconv2dwould provide 2 separate inputs to train weights with, in this case each of the previous networks weights. How are these combined afterwards?
python neural-network image-classification convolution pytorch
edited Oct 4 '18 at 0:29
timleathart
2,169726
asked Oct 3 '18 at 9:01
Benedict K.Benedict K.
1764
$endgroup$
bumped to the homepage by Community♦ 10 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
IMHO depthwise/separable convolutions provide marginal improvement and only in specific areas. IMHO ResNet is a better start overall then google's neural networks. And in my head convolutions and cross correlation are completely different things with completely different math behind. You can calculate similarity score based on the output of the layer before the last one. And weights could be completely different for the same results. So IMHO weights are not a good approximation of similarity.
$endgroup$
– keiv.fly
Oct 13 '18 at 22:27
$begingroup$
Before we consider all of those details, how are you going to train your network? What's your target?
$endgroup$
– Louis T
Jan 7 at 1:30
add a comment |
$begingroup$
2 images as input, x1 and x2 and try to use convolution as a similarity measure. The idea is that the learned weights substitute more traditional measure of similarity (cross correlation, NN, ...). Defining my forward function as follows:
def forward(self,x1,x2):
out_conv1a = self.conv1(x1)
out_conv2a = self.conv2(out_conv1a)
out_conv3a = self.conv3(out_conv2a)
out_conv1b = self.conv1(x2)
out_conv2b = self.conv2(out_conv1b)
out_conv3b = self.conv3(out_conv2b)
Now for the similarity measure:
out_cat = torch.cat([out_conv3a, out_conv3b],dim=1)
futher_conv = nn.Conv2d(out_cat)
Question is as follows:
Would Depthwise/Separable Convolutions as in the google paper yield any advantage over 2d convolution of the concatenated input. For that matter can convolution be a similarity measure, cross correlation and convolution are very similar.
It is my understanding that the
groups=2option inconv2dwould provide 2 separate inputs to train weights with, in this case each of the previous networks weights. How are these combined afterwards?
python neural-network image-classification convolution pytorch
edited Oct 4 '18 at 0:29
timleathart
2,169726
asked Oct 3 '18 at 9:01
Benedict K.Benedict K.
1764
$endgroup$
2 images as input, x1 and x2 and try to use convolution as a similarity measure. The idea is that the learned weights substitute more traditional measure of similarity (cross correlation, NN, ...). Defining my forward function as follows:
def forward(self,x1,x2):
out_conv1a = self.conv1(x1)
out_conv2a = self.conv2(out_conv1a)
out_conv3a = self.conv3(out_conv2a)
out_conv1b = self.conv1(x2)
out_conv2b = self.conv2(out_conv1b)
out_conv3b = self.conv3(out_conv2b)
Now for the similarity measure:
out_cat = torch.cat([out_conv3a, out_conv3b],dim=1)
futher_conv = nn.Conv2d(out_cat)
Question is as follows:
Would Depthwise/Separable Convolutions as in the google paper yield any advantage over 2d convolution of the concatenated input. For that matter can convolution be a similarity measure, cross correlation and convolution are very similar.
It is my understanding that the
groups=2option inconv2dwould provide 2 separate inputs to train weights with, in this case each of the previous networks weights. How are these combined afterwards?
python neural-network image-classification convolution pytorch
python neural-network image-classification convolution pytorch
edited Oct 4 '18 at 0:29
timleathart
2,169726
asked Oct 3 '18 at 9:01
Benedict K.Benedict K.
1764
edited Oct 4 '18 at 0:29
timleathart
2,169726
asked Oct 3 '18 at 9:01
Benedict K.Benedict K.
1764
edited Oct 4 '18 at 0:29
timleathart
2,169726
edited Oct 4 '18 at 0:29
timleathart
2,169726
edited Oct 4 '18 at 0:29
timleathart
2,169726
2,169726
asked Oct 3 '18 at 9:01
Benedict K.Benedict K.
1764
asked Oct 3 '18 at 9:01
Benedict K.Benedict K.
1764
asked Oct 3 '18 at 9:01
Benedict K.Benedict K.
1764
1764
bumped to the homepage by Community♦ 10 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 10 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
$begingroup$
IMHO depthwise/separable convolutions provide marginal improvement and only in specific areas. IMHO ResNet is a better start overall then google's neural networks. And in my head convolutions and cross correlation are completely different things with completely different math behind. You can calculate similarity score based on the output of the layer before the last one. And weights could be completely different for the same results. So IMHO weights are not a good approximation of similarity.
$endgroup$
– keiv.fly
Oct 13 '18 at 22:27
$begingroup$
Before we consider all of those details, how are you going to train your network? What's your target?
$endgroup$
– Louis T
Jan 7 at 1:30
add a comment |
$begingroup$
IMHO depthwise/separable convolutions provide marginal improvement and only in specific areas. IMHO ResNet is a better start overall then google's neural networks. And in my head convolutions and cross correlation are completely different things with completely different math behind. You can calculate similarity score based on the output of the layer before the last one. And weights could be completely different for the same results. So IMHO weights are not a good approximation of similarity.
$endgroup$
– keiv.fly
Oct 13 '18 at 22:27
$begingroup$
Before we consider all of those details, how are you going to train your network? What's your target?
$endgroup$
– Louis T
Jan 7 at 1:30
$begingroup$
IMHO depthwise/separable convolutions provide marginal improvement and only in specific areas. IMHO ResNet is a better start overall then google's neural networks. And in my head convolutions and cross correlation are completely different things with completely different math behind. You can calculate similarity score based on the output of the layer before the last one. And weights could be completely different for the same results. So IMHO weights are not a good approximation of similarity.
$endgroup$
– keiv.fly
Oct 13 '18 at 22:27
$begingroup$
IMHO depthwise/separable convolutions provide marginal improvement and only in specific areas. IMHO ResNet is a better start overall then google's neural networks. And in my head convolutions and cross correlation are completely different things with completely different math behind. You can calculate similarity score based on the output of the layer before the last one. And weights could be completely different for the same results. So IMHO weights are not a good approximation of similarity.
$endgroup$
– keiv.fly
Oct 13 '18 at 22:27
$begingroup$
Before we consider all of those details, how are you going to train your network? What's your target?
$endgroup$
– Louis T
Jan 7 at 1:30
$begingroup$
Before we consider all of those details, how are you going to train your network? What's your target?
$endgroup$
– Louis T
Jan 7 at 1:30
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Depthwise separable convolution followed by 1x1 convolution is an alternative to the convenient 2d convolution. They were introduced in https://arxiv.org/abs/1704.04861 to aiming on reducing the computational effort. The combination of depthwise and pointwise convolutions often learn pretty much the same representation as the normal conv.
Depthwise separable conv is an extreme case of grouped conv, where
num_groups=num_input_channels. Setting number of groups to 2 means slicing the input into two inputs across the channel dim and learning two separate filters for each sliced input. Here is nice tutorial on that. The output is combined by stacking each group's output. Often this is followed by 1x1 conv to mix the data flow between the groups.
answered Jan 11 at 15:15
Dmytro PrylipkoDmytro Prylipko
4387
$endgroup$
add a comment |
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$begingroup$
Depthwise separable convolution followed by 1x1 convolution is an alternative to the convenient 2d convolution. They were introduced in https://arxiv.org/abs/1704.04861 to aiming on reducing the computational effort. The combination of depthwise and pointwise convolutions often learn pretty much the same representation as the normal conv.
Depthwise separable conv is an extreme case of grouped conv, where
num_groups=num_input_channels. Setting number of groups to 2 means slicing the input into two inputs across the channel dim and learning two separate filters for each sliced input. Here is nice tutorial on that. The output is combined by stacking each group's output. Often this is followed by 1x1 conv to mix the data flow between the groups.
answered Jan 11 at 15:15
Dmytro PrylipkoDmytro Prylipko
4387
$endgroup$
add a comment |
$begingroup$
Depthwise separable convolution followed by 1x1 convolution is an alternative to the convenient 2d convolution. They were introduced in https://arxiv.org/abs/1704.04861 to aiming on reducing the computational effort. The combination of depthwise and pointwise convolutions often learn pretty much the same representation as the normal conv.
Depthwise separable conv is an extreme case of grouped conv, where
num_groups=num_input_channels. Setting number of groups to 2 means slicing the input into two inputs across the channel dim and learning two separate filters for each sliced input. Here is nice tutorial on that. The output is combined by stacking each group's output. Often this is followed by 1x1 conv to mix the data flow between the groups.
answered Jan 11 at 15:15
Dmytro PrylipkoDmytro Prylipko
4387
$endgroup$
add a comment |
$begingroup$
Depthwise separable convolution followed by 1x1 convolution is an alternative to the convenient 2d convolution. They were introduced in https://arxiv.org/abs/1704.04861 to aiming on reducing the computational effort. The combination of depthwise and pointwise convolutions often learn pretty much the same representation as the normal conv.
Depthwise separable conv is an extreme case of grouped conv, where
num_groups=num_input_channels. Setting number of groups to 2 means slicing the input into two inputs across the channel dim and learning two separate filters for each sliced input. Here is nice tutorial on that. The output is combined by stacking each group's output. Often this is followed by 1x1 conv to mix the data flow between the groups.
answered Jan 11 at 15:15
Dmytro PrylipkoDmytro Prylipko
4387
$endgroup$
Depthwise separable convolution followed by 1x1 convolution is an alternative to the convenient 2d convolution. They were introduced in https://arxiv.org/abs/1704.04861 to aiming on reducing the computational effort. The combination of depthwise and pointwise convolutions often learn pretty much the same representation as the normal conv.
Depthwise separable conv is an extreme case of grouped conv, where
num_groups=num_input_channels. Setting number of groups to 2 means slicing the input into two inputs across the channel dim and learning two separate filters for each sliced input. Here is nice tutorial on that. The output is combined by stacking each group's output. Often this is followed by 1x1 conv to mix the data flow between the groups.
answered Jan 11 at 15:15
Dmytro PrylipkoDmytro Prylipko
4387
answered Jan 11 at 15:15
Dmytro PrylipkoDmytro Prylipko
4387
answered Jan 11 at 15:15
Dmytro PrylipkoDmytro Prylipko
4387
answered Jan 11 at 15:15
Dmytro PrylipkoDmytro Prylipko
4387
4387
add a comment |
add a comment |
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$begingroup$
IMHO depthwise/separable convolutions provide marginal improvement and only in specific areas. IMHO ResNet is a better start overall then google's neural networks. And in my head convolutions and cross correlation are completely different things with completely different math behind. You can calculate similarity score based on the output of the layer before the last one. And weights could be completely different for the same results. So IMHO weights are not a good approximation of similarity.
$endgroup$
– keiv.fly
Oct 13 '18 at 22:27
$begingroup$
Before we consider all of those details, how are you going to train your network? What's your target?
$endgroup$
– Louis T
Jan 7 at 1:30