Number of epochs

[Mehrdad-Noori]

Hi,

Thanks for the good work. I am trying to reproduce your results and I have 2 questions.

• How many epochs (or iterations) you used to train the networks?
• Is it possible for you to release your trained models?

Thanks

[yu-rp]

Hi @Mehrdad-Noori ,

Are you referring to the diffusion model or the OoD classifier in your question?

Best

[Mehrdad-Noori]

Thanks for your reply. I was referring to the diffusion models in my question.

[yu-rp]

Hi @Mehrdad-Noori ,

• The total number of training epochs largely depends on the dataset. For instance, in our paper, we utilized 200 epochs for the PACS dataset. However, for simpler datasets like CdSprites, as you can expect, this number is significantly less.

• We would probably not release the fine-tuned diffusion model. The reason is that the fine-tuning part is not the primary focus or core contribution of our paper. Moreover, the fine-tuning part of our implementation is a relatively independent component. Currently, there are exemplary repositories and resources dedicated to the fine-tuning of stable diffusion models. They often offer better efficiency or better performance in the few-shot setting. We would like to acknowledge that using their method to substitute the fine-tuning part in our implementation may potentially lead to even more optimal results. All you need is a diffusion model on the source domain, no matter how you get it. :)

Best

[Mehrdad-Noori]

Thank you very much for your prompt respons. The reason I asked for the fine-tuned models is to reproduce the results of the paper and try to improve them. Actually, I tried to fine-tune a diffusion model on "art_painintg" domain of the PACS dataset for more than 500 epochs using the defualt setting mentiond in your repository. The training results are satisfying and it seems that the DM learned the source distribution very well. But when I run the step 2, "transformation" in the repo, the transofrmed results which are saved as png files are not very good, and it seems that the class of the most of the samples are changed. Some of them are attached here:

I want to make sure that every thing I have done is correct so that I would to be able to continue to the next steps. Maybe I am missing something. In the paper, it is mentioned that this process is iterative and denoising steps of diffusion model countine until certain confidence at the classifier output is reached. In these examples, it seems that there is no iterative process and only some images are saved at this step. I would be gratful if you could also let me know how we can produce these images in an offline form and then use them for the OOD classifier.

[yu-rp]

Hi @Mehrdad-Noori,

If the category isn't well preserved, it suggests that too much noise has been introduced. By lowering the noise level, category information will be better retained. This can be achieved by selecting a smaller starting time index for the diffusion process. Of course, a compromise to consider is that the domain transfer may not be as comprehensive.

As mentioned in our readme, the transformation step only produces images corresponding to various noise levels. The iterative process takes place during the inference phase.

Best

[Mehrdad-Noori]

Thanks. I will use this network for the next step. Currently, I am training an ERM model using DomainBed. So the inference code will not feed these generated images to the OOD classifier and will create some samples during the 'DSI.main' based on the iterative process explained in the paper? Another question is that what should I feed as 'lpcd' to DSI/main.py?

[yu-rp]

Hi @Mehrdad-Noori ,

• The samples are generated during the Transformation period. After generating the samples, we use them in the Inference period.
• lpcd is a .json file containing the path of the generated samples. One example of how such .json file should be is here.

Best