Evaluating Generative Adversarial Networks: A Topological Approach
Why this work is in the frame
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Bibliographic record
Abstract
Generative adversarial networks (GANs) are an approach to generative modelling using deep learning methods, such as convolution neural networks. Evaluating the performance of GANs has been a challenging task. In this paper, we will show how concepts from algebraic topology, and in particular persistent homology can be used for comparing the geometric and topological features of the latent manifold of real data with those of generated ones. We built a Vietoris-Rips complex to present persistence diagrams. As an evaluating metric between two diagrams of manifolds, we apply a framework which is a reformulation of the Wasserstein distance as an Optimal transport problem, called the WOT Distance. We compare the WOT Distance with the other topological structure metrics, Geometric score (GS) and Topological Distance (TD) on various data sets. Evaluation results demonstrate that our method achieves superior performance in GANs learning.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it