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Thank you very much your pytorch implementation for L2P!
I have a question about prompt selection.
In the paper, they use prompt frequency based weight to select diverse prompt, but i think i can't find that part in the code.
I think I can't find that part not only in your code but also in the official jax-based code, so could you let me know if there's anything I'm missing?
Thank you very much for your work!!!
The text was updated successfully, but these errors were encountered:
You're right. In the paper, they use frequency-based weight to select a diverse prompt.
I also could not find the explicit part in the official jax code, and could not implement.
According to a recent author's explanation (linked by @pmy0792 above), they implemented the method simply and effectively using config.use_prompt_mask.
Fortunately use_prompt_mask is already implemented in my code, and I think you can change this option to True and apply a method that selects a diverse prompt.
However, they said in paper that the method was applied only to 5-datasets, not Split-CIFAR100.
I recently implemented the PyTorch code for 5-datasets, and I updated it, so check it.
If you have any additional comments, please feel free to let me know.
Thank you very much your pytorch implementation for L2P!
I have a question about prompt selection.
In the paper, they use prompt frequency based weight to select diverse prompt, but i think i can't find that part in the code.
I think I can't find that part not only in your code but also in the official jax-based code, so could you let me know if there's anything I'm missing?
Thank you very much for your work!!!
The text was updated successfully, but these errors were encountered: