Recently I have come up with a VGG16 model for my binary classification task. I have relatively simple signal images
Therefore (maybe?) other deeper models like resnet18
and Inceptionv3
were not as good. As known, VGG uses 3x3
filters for convolving the images to make feature maps. I have tried several hyper-parameters to get a desired performance. However, there are still some things I need to do. I was thinking of replacing the 3x3
conv
filters with 3x1
followed by 1x3
filters to reduce the compute. I think it will definitely do so considering the multiplications (9 operations for 3x3
and 6 for 3x1
followed by 1x3
).
Then I came to think: If I replace all the 3x3
filters with separable filters, will I get any performance improvement?
What are the benefits of replacing 3x3
filters with separable ones?
Thanks