Progressively Growing of Least Squares Generative Adversarial Networks
Bibliographic record
Abstract
In the past decade, generative models have seen exponentially use in the world of computer vision. One architecture that has consistently contributed to this domain is generative adversarial networks. These networks can produce outstanding results and very realistic appearing images. They do not however come without their downfalls, as they tend to be extremely unstable when training for resolutions beyond 64x64. As a result, several solutions have been proposed to combat stability and other issues found during training such as a lack of variation in images produced. The first set of solutions focus on using a variety of different loss functions such as the Wasserstein distance loss function or the least squares loss function. While other solutions propose altering the architecture used or even the training methodology which the networks undergo. To build upon the success of other solutions this paper will propose an architecture which grows during training to allow for high resolution images to be produced. This solution will combine the efforts of multiple other ones while also contributing novel changes to the GAN architecture. As an outcome, this report will showcase the new proposed approach and its ability to produce comparable results to other state-of-the-art solutions.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".