Comments (2)
I am not the author. Based on my understanding, the authors propose an algorithm that leverages the discriminative ability of existing diffusion models for classification tasks. That means the performance is determined by the algorithm used and the diffusion models. And I personally think diffusion models are more important to the performance improvements. Nonetheless, developing effective algorithm is still valuable. That is why I ask the author to compare with exact log-likelihood
, another alg, see #2
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@pengzhangzhi Hi, thanks for your reply! I've just checked #2 and the Score SDE paper, and found your observation quite inspiring. It seems there's some inherent connection between probability flow ODE and class discrimination. As the auther stated in #2 , how to effectively and efficiently estimate log-likelihood could be a problem to be solved next.
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Related Issues (20)
- Possible to run on CPU? HOT 1
- Getting pred. probabilities HOT 1
- Public Benchmarking HOT 2
- Example of the argument "subset_path" HOT 2
- No such operator xformers HOT 2
- Question about the code running speed HOT 1
- Example about multiple workers HOT 2
- about add noise implementation HOT 2
- Question about the inference hyper-parameters HOT 2
- Question about results of cifar 100 dataset HOT 1
- multiplication of the encoded image by 0.18215 HOT 1
- Error on loading 'diffusion/imagenet_class_index.json' HOT 1
- The diffusion.datasets package in print_dit_acc.py HOT 1
- About the prediction probability HOT 1
- About test samples for computing accuracy HOT 1
- About the diffusion model implementation HOT 6
- About CUDA and xformers HOT 4
- A demonstration Colab?
- conda env create takes forever, anyone has the same issue? HOT 6
- question about 'SD Features' HOT 6
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