Improving the Training of the GANs with Limited Data via Dual Adaptive Noise Injection
Bibliographic record
Abstract
Recently, many studies have highlighted that training Generative Adversarial Networks (GANs) with limited data suffers from the overfitting of the discriminator (D). Existing studies mitigate the overfitting of D by employing data augmentation, model regularization, or pre-trained models. Despite the success of existing methods in training GANs with limited data, noise injection is another plausible, complementary, yet not well-explored approach to alleviate the overfitting of D issue. In this paper, we propose a simple yet effective method called Dual Adaptive Noise Injection (DANI), to further improve the training of GANs with limited data. Specifically, DANI consists of two adaptive strategies: adaptive injection probability and adaptive noise strength. For the adaptive injection probability, Gaussian noise is injected into both real and fake images for generator (G) and D with a probability p, respectively, where the probability p is controlled by the overfitting degree of D. For the adaptive noise strength, the Gaussian noise is produced by applying the adaptive forward diffusion process to both real and fake images, respectively. As a result, DANI can effectively increase the overlap between the distributions of real and fake data during training, thus alleviating the overfitting of D issue. Extensive experiments on several commonly-used datasets with both StyleGAN2 and FastGAN backbones demonstrate that DANI can further improve the training of GANs with limited data and achieve state-of-the-art results compared with other methods. Codes are available at https://github.com/zzhang05/DANI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".