Unsupervised Image Categorization Based on Variational Autoencoder and Student’s-T Mixture Model
Bibliographic record
Abstract
In this work, a novel generative robust image catego-rization approach is developed based on variational autoencoder (VAE) and Student's-T Mixture Model (STMM). The network structure composed of VAE, STMM and Convolutional Neural Network (CNN) generates data. More specifically, first, a cluster is chosen using the STMM. Then, a latent representation is extracted from the selected cluster through a CNN encoder. After that, an observation is generated based on another CNN through a decoding process. The proposed model is learned through variational inference where the Evidence Lower Bound is optimized according to Stochastic Gradient Descent(SGD) and the reparameterization trick. Based on our experimental results, the proposed generative clustering approach is able to outperform classical clustering approaches (e.g. K-means, Gaussian Mixture Models) and other related generative clustering approaches. Furthermore, we show that our generative model is able to generate highly realistic samples without using any supervised information during training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".