currently have subtle ﬂaws that allow detection [62], but improvements in generative models may
make this more difﬁcult. Generative models also reﬂect the biases in the datasets on which they
are trained. As many large datasets are collected from the internet by automated systems, it can be
difﬁcult to remove these biases, especially when the images are unlabeled. If samples from generative
models trained on these datasets proliferate throughout the internet, then these biases will only be
reinforced further.
On the other hand, diffusion models may be useful for data compression, which, as data becomes
higher resolution and as global internet trafﬁc increases, might be crucial to ensure accessibility of
the internet to wide audiences. Our work might contribute to representation learning on unlabeled
raw data for a large range of downstream tasks, from image classiﬁcation to reinforcement learning,
Denoising Diffusion Probabilistic Models