Genaralizing Generative Models: Application to Image Super-Resolution
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
Generative models such as neural networks or markov random fields are difficult to extend beyond a limited spatial extent due to the large configuration spaces involved. Their usage in applications such as super resolution is thus problematic, despite their capacity for learning rich descriptions from local receptive fields. For example, due to model complexity, the number of parameters grows exponentially with respect to the size of the receptive field. In this paper we show how to deal with this limitation using a Convolutional Deep Boltzmann Machine (ConvDBM) for modelling distributions on large receptive fields with a controllable number of parameters. In particular, we show that i) by weight sharing and joint training over the second hidden layer, the prior distribution on a large receptive field can be represented properly using a small number of parameters, ii) scaling up to high resolution images can be achieved by applying the resulting ConvDBM sequentially with tiled weights. Experimental results are presented that show successful application of this approach to the problem of super-resolution.
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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 | 0.002 |
| Open science | 0.001 | 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