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
Hi, I came across your paper a few weeks ago.
I have a dataset that constantly grows, like every couple of weeks. The growth is both in terms of more examples of a set of known classes as well as new classes being added.
Is it possible to use this method to keep a reduced dataset of the old images?
For eg: I have 10k images that I want to distill into 100. Then I get a new batch of 200 images.
How would I retrain a model "from scratch" using this combination of distilled and raw images?
I'm a grad student focussing on HPC, so I'm sorry if these questions are silly. But I would greatly appreciate any feedback, thank you!
The text was updated successfully, but these errors were encountered:
Hi, I came across your paper a few weeks ago.
I have a dataset that constantly grows, like every couple of weeks. The growth is both in terms of more examples of a set of known classes as well as new classes being added.
Is it possible to use this method to keep a reduced dataset of the old images?
For eg: I have 10k images that I want to distill into 100. Then I get a new batch of 200 images.
How would I retrain a model "from scratch" using this combination of distilled and raw images?
I'm a grad student focussing on HPC, so I'm sorry if these questions are silly. But I would greatly appreciate any feedback, thank you!
The text was updated successfully, but these errors were encountered: