- The paper uses 5,000 images as the training set (am I correct?) . I think the training set size is too small, and is easily memorized with sufficient long learning by large models such as SD 2 . What I am concerned about is what proportion of data is memorized when training with a huge set.
- This method seems to only work for generative models that can be fine-tuned as an in/outpainting model.
- Since the model has been fine-tuned, is it still capable of reflecting the memorization of the original model?
- Although the approach is novel and interesting, it lacks strong evidence to support its effectiveness as an evaluation method for memorization.
- This method seems to only work for generative models that can be fine-tuned as an in/outpainting model.