Closed jiequanz closed 5 years ago
@Jiequannnnnnnnnn
Hi Jiahui,
Thanks for your reply! I have some follow up questions.
For my first question, I understand that there are two convolutions in one block, and k is the kernel size. What confused me was w1^2. Here is my understanding: For one convolution, we have w1 filters, each of size k*k. So the number of parameters is w1 k k(weights) + 1 (bias) .
What does FLOP stand for?
FLOPs is the number of multi-adds.
Hi Jiahui, thanks for your work and I have some conceptual questions (maybe very basic).
In section 3.1 of the paper, in the vanilla residual network, why is the number of parameter 2 W1^2 k^2? I don't get where W1^2 comes from. Why is the final HR image representation S^2 * 3?
In the other issue (wdsr_b increase channel problem), I noticed this "48->48x6(1x1)->48x0.8->48" and "32->192(1x1)->25(1x1)->32". Do you mind explaining this? How do you determine how large the third number is (48*0.8 and 25)?
In section 3.2, what does low-rank convolution mean here? The paper says it factorizes a large convolution kernels to two low-rank convolution kernels. I think the two low-rank kernels are the 1x1 and 3x3 kernel, but what is the "large convolution kernel" refering to?
Is there a reason why these scale numbers (2-4,6-9) are chosen? Have you tried what happens if r is very large, such as 20? Also, what makes you come up with this 1x1 convolution to solve this?
How do you determine the number of residual box?
Thanks for reading! Sorry if some of them are too basic. I am really interested in the paper, but I am stuck by these questions.