A Deep Autoencoder With Novel Adaptive Resolution Reconstruction Loss for Disentanglement of Concepts in Face Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Among the different categories of natural images, face images are very important because of their broad range of applications. One challenging topic of face processing by computers is extracting information related to only specific concepts from face images without the help of labels. In this article, we propose a deep autoencoder model for extracting facial concepts based on their scales. A novel adaptive resolution (AR) reconstruction loss is introduced for training the autoencoder model. With the help of this new reconstruction loss, the deep autoencoder model is able to receive a real face image and compute its representation vector, which not only makes it possible to reconstruct the input image faithfully but also separates the concepts related to specific scales. We demonstrate that the autoencoder trained using the AR reconstruction loss is able to outperform benchmark models in generating faithful and high-quality reconstructions of real face images and is able to successfully transfer the facial concepts associated with a specific scale from one input image to another.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it