Semantic Segmentation Using a GAN and a Weakly Supervised Method Based on Deep Transfer Learning
Bibliographic record
Abstract
Semantic image segmentation is of crucial importance to many applications, such as autonomous driving, robot vision, and scene understanding. However, the border of a segmented image tends to be rough, and the labeling process is tedious and labor-intensive. Therefore, this study is the first proposing to use a deep generative adversarial network (GAN) with double-layered upsampling based on max-pooling indexed deconvolution. Our proposed upsampling method replaces the bilinear interpolation upsampling method; i.e., we fuse the deep deconvolution method by saving the indices of relative locations of the max weights computed during pooling. Combined with the deep GAN, our upsampling method can improve the extraction of low-resolution features, and compensate for the loss of the image size. To further reduce the whole network's dependence on labeled datasets, a weakly supervised feedback method is proposed. The unlabeled data can improve the generalization ability of the model. Considering the generalization to unseen image domains, we introduce transfer learning based on a deep GAN and a weakly supervised method. The segmentation model using the trained data in the source domain can obtain good segmentation in the target domain using transfer learning. Extensive experiments in various domains demonstrate the advantages of the proposed method compared to the generalization ability of semantic segmentation. This method also significantly decreases the dependence on labeled data and ensures the network accuracy.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".