Comments (1)
It appears the score network is also conditioned on the augmentation parameters during training, which gets mapped into a embedding just like the noise (in fact they are summed together). So it can be thought of as training a score network over an ensemble of different data distributions. It looks like at generation time the augmentation parameters passed to the network are all zeros.
My educated guess is that it may still be possible for the augmentations to leak into the generation, it depends on to what extent the network has learned that the zero vector indeed corresponds to the real data distribution.
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Related Issues (20)
- params[x] in this process with sizes [x, x] appears not to match sizes of the same param in process 0 HOT 2
- Is it possible to also share the code for Figure 3 in the paper HOT 1
- VP checkpoints are trained using VE-scaling? HOT 2
- About the Checkpoint
- Could you please provide us imagenet-uncond checkpoint?
- Can not reproduct the result in Table 2. HOT 5
- ERROR when using multi-gpu training HOT 7
- Question about parameter tuning
- On checkpoint for 256*256 images
- Different Phases of Sampling
- About hyper-parameters selection
- AssertionError: Torch not compiled with CUDA enabled
- Is it possible to also share the code for Figure 1b (ideal denoiser outputs) in the paper
- Deterministic sampling and sampling steps
- Wrong beta_D coefficient for VP during sampling? HOT 1
- How to select options shown in Table2 for training VP/VE models HOT 1
- DataLoader worker (pid xxxx) is killed by signal HOT 1
- .
- Is the original loss of VP/VE implemented (i.e. network predict epsilon)? HOT 1
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