MixedGAN: Facilitating GAN for Domain Adaptation Learning based on Mixing Different Domain Images for Semantic Segmentation
Bibliographic record
Abstract
Convolutional neural network-based semantic segmentation methods rely on the supervision of pixel-level labels, but since pixel-level labelling is time-consuming and labor-intensive, research has shifted to Unsupervised domain adap-tation (UDA), which is mainly used to migrate the learned knowledge from one domain to another, so that synthetic data can be used to help the model learn. Synthetic data can be used to help the model to learn, but the accuracy is not high compared to supervised learning. The reason for the low accuracy of UDA is the gap between domains. In this paper, a new UDA model is proposed to reduce the gap between domains in two steps. The first step is to mix images from two domains and generate pseudo-labels for the generated images, which helps to close the image distribution between domains. In the second step, the Generating Adversarial Neural Network (GAN) is used to distinguish the source of the feature map to further reduce the inter-domain gap. After several experiments, the accuracy of this model is much better than some classical DA models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".