Latent Representation of Microstructures Using Variational Autoencoders with Spatial Statistics-Space Loss
Bibliographic record
Abstract
We propose the use of Cross-Entropy on 2-point spatial statistics (CESS) as a reconstruction loss term for a variational autoencoder, creating a small latent-space representation of microstructures from which microstructures can be reconstructed. A prospective application of small reversible microstructure representations is more efficient optimization for materials properties in latent space. In Materials Science, 2-point spatial statistics have been shown to be good representations of microstructure properties, and have many desirable invariances (translation, phase label, inversion). To our knowledge, we are the first to demonstrate a system that successfully creates latent representations of realistic simulations of microstructures by using an error term that minimizes the distance between input and reconstruction in spatial statistics space. We also demonstrate some promising preliminary qualitative results that show improved quality of reconstructions using CESS loss. We demonstrate compression from 224×224 binary microstructure images to 14×14 latent representations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".