You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Hey! I think GLIDE is a wonderful work. But I have a question about CLIP training on nosied images.
I want to know why CLIP can be trained on nosied images. I think if t (range from 0 to 1000) is large(maybe close to 500 or more), then the noised images hardly contain any semantic information. In this case, I want to know CLIP model how to encode similar features from noised images and text and I also think it may cause model to not converge (because it is hard to encode similar features between noised images and text)
The text was updated successfully, but these errors were encountered:
Hey! I think GLIDE is a wonderful work. But I have a question about CLIP training on nosied images.
I want to know why CLIP can be trained on nosied images. I think if t (range from 0 to 1000) is large(maybe close to 500 or more), then the noised images hardly contain any semantic information. In this case, I want to know CLIP model how to encode similar features from noised images and text and I also think it may cause model to not converge (because it is hard to encode similar features between noised images and text)
The text was updated successfully, but these errors were encountered: