Dear Author, I am really appreciated by your great works. Thanks a lot.
Now I have a questions of implementation on Algorithm,
Q1.
As you mentioned in the paper, if c is lower than r, then the current minibatch modality are added to the buffer for true negative.
then may I comprehend that there is other buffers, which is similar ones like queues for each modality?
Q2.
For determining whether the c_k of x_k and c_k of y_k are lower than the hyper parameter r,
if I detect the c_k of x_k and c_k of y_k among the queue_x and queue_y are truly lower than r,
how can I implement it in pytorch code?
Q3.
If you do not mind, I wanna get your full code, so, can I get the part of them if you are reluctant to send the full code?
I really feel thirsty and feel really really fascinated by your work.
If you can, please send me email : key2317@naver.com
I DO promise not to distribute any other guys if there is no permission to.
Dear Author, I am really appreciated by your great works. Thanks a lot.
Now I have a questions of implementation on Algorithm,
Q1. As you mentioned in the paper, if c is lower than r, then the current minibatch modality are added to the buffer for true negative. then may I comprehend that there is other buffers, which is similar ones like queues for each modality?
Q2. For determining whether the c_k of x_k and c_k of y_k are lower than the hyper parameter r, if I detect the c_k of x_k and c_k of y_k among the queue_x and queue_y are truly lower than r, how can I implement it in pytorch code?
Q3. If you do not mind, I wanna get your full code, so, can I get the part of them if you are reluctant to send the full code? I really feel thirsty and feel really really fascinated by your work. If you can, please send me email : key2317@naver.com I DO promise not to distribute any other guys if there is no permission to.
Again, Thank you for your attention.