Closed samsara-ku closed 1 year ago
We didnot use any pretrained networks for visualization. t-SNE itself is a dimenation reduction tool, and it is well encapsulated as a package in python. You can refer to sklearn.mainfold.TSNE for more details.
Sorry for bothering you, but how could that?
To my best knowledge, the shpae of t-SNE input should be 2 dimension like mnist image size, (28, 28).
Did you compress the images shape like (batch, channel, width, heigth) into 2D like (batch, ~~~)?
If possible, can you share any piece of codes for t-SNE plotting?
You can send an email to yuhu520@mail.ustc.edu.cn. I will share you the t-SNE code.
Thank you! :)
Hi, first thanks to your nice work.
I have a question about the Fig. 2., the t-SNE plot.
How to embed the images(i.e. hazy, clear, synhazy, synclear) into 2D space for plotting t-SNE?
Did you use any pre-trained network like ResNet?
I just want to know the way how to embed the image into vector when you plotting t-SNE, but in the paper there is no information about that.