Training Diffusion Classifiers with Denoising Assistance
Chandramouli Sastry Sri Harsha Dumpala Sageev Oore
Score-matching and diffusion models have emerged as state-of-the-art
generative models for both conditional and unconditional generation.
Classifier-guided diffusion models are created by training a classifier on
samples obtained from the forward-diffusion process (i.e., from data to noise).
In this paper, we propose denoising-assisted (DA) classifiers wherein the
diffusion classifier is trained using both noisy and denoised examples as
simultaneous inputs to the model. We differentiate between denoising-assisted
(DA) classifiers and noisy classifiers, which are diffusion classifiers that
are only trained on noisy examples. Our experiments on Cifar10 and Imagenet
show that DA-classifiers improve over noisy classifiers both quantitatively in
terms of generalization to test data and qualitatively in terms of
perceptually-aligned classifier-gradients and generative modeling metrics.
Finally, we describe a semi-supervised framework for training diffusion
classifiers and our experiments, that also include positive-unlabeled settings,
demonstrate improved generalization of DA-classifiers over noisy classifiers.