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>
2.9 KiB
mnist_collapse — collapse and grounding-rescue on REAL MNIST images (external validity)
Claim tested: everything so far used a synthetic sandbox with a zero-error decoder oracle. Do model collapse and its rescue by grounding also appear on real images with a classifier oracle — i.e. is the effect real, not a synthetic artefact?
Setup (Layer 1.5, real-data tier). The generative model is a convolutional VAE (the model
in which generative collapse was first observed). Each generation a fresh VAE is trained from
scratch on the previous VAE's own generated digits, plus a fraction g of fresh real MNIST
images (grounding). K = 30 modes = digit class × stroke-thickness bin (S=3), Zipf-resampled so
the rarest ~18 modes form a real tail. The oracle is a frozen CNN (digit class) + deterministic
thickness bin; its mode accuracy ≈ 98.5% (recorded in manifest.json with the full 30×30
confusion matrix) is the measurement-noise floor. Two arms — dry (g = 0) vs grounded (g = 0.1) —
n = 6000 images/generation, 15 generations, 4 replicates.
Symbols
- mode = (digit class, stroke-thickness bin);
p*= Zipf truth over the 30 modes;p̂= the VAE's oracle-measured mode distribution. g= grounding fraction (share of real MNIST images each generation). forward-KL = distance from truth; support = distinct modes alive;H= diversity; tail truth-mass alive = fraction of the rare tail retained.
The four panels (dry = red, grounded = green; band = 95% CI over 4 reps)
- Forward-KL. Dry climbs from ~0.5 to ~18 (the VAE drifts far from truth); grounded stays near the floor. Collapse is real on images.
- Support. Dry collapses from all 30 modes to ~1 (the VAE ends up emitting a single blurry mode); grounded holds all 30.
- Tail truth-mass alive. Dry's rare tail is wiped out (→ 0.06); grounded keeps the whole tail.
- Heterozygosity. Dry diversity → 0; grounded holds
H ≈ 0.9.
See mnist_montage.png for the eyeball version: gen-0 digits are varied and recognisable; by
gen 12–15 the dry lineage has degenerated into one blurry blob.
Takeaway
Model collapse and its arrest by a small dose of real data reproduce on real MNIST images with a
learned classifier oracle — external validity for the whole Layer-1.5 story. Note the VAE needs
~10% grounding here (vs ~5% for the synthetic histogram), consistent with the grounding finding
that trained neural models need somewhat more grounding than the exact operator. This is
confirmation-only (signs, not magnitudes; blueprint §3.5) — the exact synthetic oracle remains
the anchor for every quantitative claim, and the oracle confusion matrix is the recorded noise floor.
Falsifier (not triggered): if the dry VAE had shown no diversity loss, or grounding had failed to
arrest it, the external-validity claim would fail.