Unpaired image neural style transfer based on Non-Local-Attention-Cycle-Consistent adversarial network
Bibliographic record
Abstract
Existing image translation methods already enable style transfer on unpaired data. Although these methods have yielded satisfactory results, they still result in changing the background while changing the object. One reason is that when using convolutional neural networks, global information is lost as the number of network layers increases, and the absence of an effective sensory field leads to the failure to generate high-quality results. This paper proposed a Non-Local-Attention-Cycle-Consistent Adversarial Networks for unpaired images style transfer. The no-local-attention can quickly capture long-range dependencies, better extracts global information, ensures effective focus on the foreground while preserving the background, and can be easily embedded into the current network architecture. Experiments are conducted on neural style transfer task with public dataset, this model can obtain the better result than CycleGAN. It allows better attention to structural features rather than just textural features. It can reconstruct some of the content lost by CycleGAN. Recent research has also demonstrated that the optimizer has an impact on the performance of the network. This paper applies the Nadam optimizer and find that this improves training process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".