NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image\n Generation
Bibliographic record
Abstract
In this paper, we present a non-parametric structured latent variable model\nfor image generation, called NP-DRAW, which sequentially draws on a latent\ncanvas in a part-by-part fashion and then decodes the image from the canvas.\nOur key contributions are as follows. 1) We propose a non-parametric prior\ndistribution over the appearance of image parts so that the latent variable\n``what-to-draw'' per step becomes a categorical random variable. This improves\nthe expressiveness and greatly eases the learning compared to Gaussians used in\nthe literature. 2) We model the sequential dependency structure of parts via a\nTransformer, which is more powerful and easier to train compared to RNNs used\nin the literature. 3) We propose an effective heuristic parsing algorithm to\npre-train the prior. Experiments on MNIST, Omniglot, CIFAR-10, and CelebA show\nthat our method significantly outperforms previous structured image models like\nDRAW and AIR and is competitive to other generic generative models. Moreover,\nwe show that our model's inherent compositionality and interpretability bring\nsignificant benefits in the low-data learning regime and latent space editing.\nCode is available at https://github.com/ZENGXH/NPDRAW.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".