Wednesday 09 April 2025
Deep learning models have come a long way in generating realistic images, but they still struggle with producing high-quality results when trained on noisy or corrupted data. This is because noise can easily get amplified during the training process, leading to poor performance and inaccurate outputs.
Researchers have been working on developing techniques to improve the quality of generated images, even when the training data is noisy. One promising approach is called denoising score distillation (DSD), which involves pre-training a model on noisy data and then using it to refine another model’s output.
The key idea behind DSD is to use the pre-trained model as a teacher to guide the training of the second model, known as the student. The teacher model is trained on the noisy data, while the student model is trained on clean data or partially clean data. By comparing the outputs of both models, the student can learn how to correct for noise and produce more accurate results.
In a new paper, researchers describe an improved version of DSD that uses a technique called score matching to further refine the generated images. Score matching involves training the model to match the probability distribution of the input data, rather than simply generating a realistic image. This approach has been shown to improve the quality and realism of generated images.
The researchers tested their method on several datasets, including CIFAR-10, FFHQ, CelebA-HQ, and AFHQ-v2. They found that their DSD model was able to generate high-quality images that were comparable to those produced by state-of-the-art models trained on clean data.
One of the key advantages of DSD is its ability to handle noisy or corrupted data. By pre-training the teacher model on noisy data, it can learn how to correct for noise and produce more accurate results. This makes it particularly useful for applications where high-quality training data is not available or is difficult to obtain.
The researchers also experimented with using DSD in combination with other techniques, such as consistency loss and proximal FID. Consistency loss involves adding a penalty term to the loss function that encourages the model to produce consistent results when given different inputs. Proximal FID is a measure of the similarity between the generated images and the real data.
By combining these techniques, the researchers were able to further improve the quality and realism of their generated images. They found that DSD was particularly effective at handling noisy or corrupted data, and that it could produce high-quality results even when trained on partially clean data.
Cite this article: “Revolutionizing Generative Modeling: One-Step High-Quality Image Generation with Denoising Score Distillation”, The Science Archive, 2025.
Deep Learning, Image Generation, Denoising Score Distillation, Noise Reduction, Image Quality, Probability Distribution, Score Matching, Generative Models, Noisy Data, Corrupted Data
