Evaluation of Latent Space Learning with Procedurally-Generated Datasets of Shapes
Bibliographic record
Abstract
We compare the quality of latent spaces learned by different neural network models for organizing collections of 3D shapes. To accomplish this goal, our first contribution is to introduce a synthetic dataset of shapes with known semantic attributes. We use a procedural method to generate a dataset comprising four categories, with a total of over 10,000 shapes, providing a controlled setting for studying the properties of latent spaces. In contrast to previous work, the synthetic shapes generated with our method have a more realistic appearance, similar to objects in manually-modeled collections. We use 8,800 shapes from the generated dataset to perform a quantitative and qualitative evaluation of the latent spaces learned with a set of representative neural network models. Our second contribution is to perform the quantitative evaluation with measures that we developed for numerically assessing the properties of the latent spaces, which allow us to objectively compare different models based on statistics computed on large sets of shapes.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".