Giorgio Gilestro
79bbc45f41
Confirms model collapse and its arrest by grounding on REAL images, not just the synthetic sandbox. A conv VAE (the canonical generative-collapse model) is retrained each generation on its own generated digits, with a fraction g of fresh real MNIST mixed in. Modes = digit class x stroke- thickness bin (K=30, Zipf, ~18 tail modes); the oracle is a frozen CNN + deterministic thickness at 98.5% mode accuracy (30x30 confusion matrix recorded in the manifest as the measurement-noise floor). Result (4 reps): dry (g=0) collapses to a single mode -- forward-KL 0.5->18, support 30->1, tail 1.0->0.06, H->0 -- while 10% grounding holds all 30 modes (KL~0.6, full tail, H~0.9). Signs, not magnitudes (blueprint 3.5); the exact synthetic oracle stays the quantitative anchor. The VAE needs ~10% grounding vs the synthetic histogram's ~5%, consistent with the grounding finding that trained nets need more than the exact operator. Plugs into the existing data-agnostic contract (metrics/grounding/output reused verbatim): mnist_data (thickness bins, class x thickness bijection, MnistSampler), mnist_oracle (ClassifierOracle + confusion matrix), mnist_vae (ConvVAEGenerator), mnist_loop (run_mnist_lineage), kind= mnist_lineage dispatch, MnistCfg/OracleCfg. Figures: plot_mnist (parquet- only) + mnist_montage (eyeball diagnostic showing digits degenerate to one blurry mode). make mnist / make env-mnist, kept out of the make neural loop. 99 tests green (+5 torchvision-gated). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
33 KiB
33 KiB