End-to-End Generative Adversarial Face Hallucination Through Residual In Internal Dense Network
Bibliographic record
Abstract
Face hallucination has been a highly attractive computer vision research topic in recent years. It is still a particularly challenging task since the human face has a complex and delicate structure. In this paper, we propose a novel network structure, namely end-to-end Generative Adversarial Face Hallucination through Residual in Internal Dense Network (GAFH-RIDN), to hallucinate an unaligned tiny (32×32 pixels) low-resolution face image to its 8× (256×256 pixels) high-resolution counterpart. We propose a new architecture called Residual in Internal Dense Block (RIDB) for the generator and exploit an improved discriminator, Relativistic average Discriminator (RaD). In GAFH-RIDN, the generator is used to generate visually pleasant hallucinated face images, while the improved discriminator aims to evaluate how much input images are realistic. With continual adversarial learning, GAFH-RIDN is able to hallucinate perceptually plausible face images. Extensive experiments on large face datasets demonstrate that the proposed method significantly outperforms other state-of-the-art methods.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".