title: Factorial Code Forge Lab
emoji: 🧪
colorFrom: indigo
colorTo: blue
sdk: static
app_file: index.html
pinned: false
Factorial Code Forge
Factorial Code Forge mixes two statistically independent non-Gaussian sources and asks a tiny autoencoder to recover a useful two-dimensional code. In the experimental variant, two adversarial predictors try to infer each latent coordinate from the other while the encoder tries to make those predictions fail. Reconstruction and variance constraints prevent a constant-code shortcut.
The historical anchor is Schmidhuber's 1992 Learning Factorial Codes by Predictability Minimization. The modern experiment compares predictability minimization with an architecture-matched reconstructive encoder, PCA whitening, and FastICA. It is a new controlled benchmark, not a reproduction of the original paper's implementation or datasets.
Verified results
Models trained on 20,000 mixed samples and were measured once on 5,000 new samples:
| Code | Absolute correlation | Symmetric MI estimate |
|---|---|---|
| Autoencoder control | 0.02200 | 0.22345 |
| Online neural-adversarial ablation | 0.01886 | 0.22678 |
| Validation-selected predictability minimum | 0.01158 | 0.00280 |
| PCA whitening | 0.02039 | 0.22275 |
| FastICA | 0.00590 | 0.00326 |
The selected 12-parameter linear codec reconstructed the mixtures at
1.08e-14 MSE. Its rotation was selected using polynomial cross-predictors on a
held-out portion of the training set, never the test set. The online neural
minimax game did not improve nonlinear dependence and is retained as a negative
stability result rather than omitted.
Reproduce
uv run python projects/factorial-code-forge/train.py
Hosted showcase
This free static Space preserves the complete original Gradio source, trained artifacts, evaluation files, and local launch requirements. Hugging Face now requires PRO for CPU-backed Gradio hosting, so the public landing page is static while the checked-in app.py remains the authoritative runnable demo.