Application of Smoothing Labels to Alleviate Overconfident of the GAN's Discriminator
Bibliographic record
Abstract
A Deep Convolutional Generative Adversarial Network (DCGAN) suffers from a vanishing gradient issue in the generator due to the overconfidence of the discriminator.This paper explores the effects of using noise injection and gradually changing label smoothing (CLS) towards hard labels and two-sided label smoothing to enhance the stability of the DCGAN.Different models are trained on CIFAR-10 datasets that contains 60,000 32×32 color images divided into 10 categories and CIFAR-100 datasets that contains 60,000 32×32 color images divided into 100 categories, compared with each other using Fré chet Inception distance (FID), and Inception Score (IS) evaluation metrics.A noticeable improvement in generalization was found in almost all cases, and the best was when using CLS for both real and fake labels of two-sided smoothing labels.The modified DCGAN performs better than traditional DCGAN, boosting the best Fré chet Inception distance from 132.31 to 95.52 and the Inception Score (IS) from 25.123 to 64.27 on the CIFAR-10 dataset, the FID from 137.84 to 109.42, and the IS from 19.65 to 61.04 on the challenging CIFAR-100 dataset.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".