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>
59 lines
2.1 KiB
YAML
59 lines
2.1 KiB
YAML
experiment: mnist_collapse
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kind: mnist_lineage
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seed: 20260705
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n_replicates: 4
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# (Layer 1.5 external-validity tier; maps to Layer-1 E1/E2 and neural collapse/grounding): does
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# model collapse — and its rescue by grounding — appear on REAL MNIST images, not just the
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# synthetic sandbox? A convolutional VAE (the canonical model in which generative collapse was
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# first observed) is retrained each generation on the previous VAE's own generated images, plus a
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# fraction g of fresh REAL MNIST images (grounding). Modes = digit class x stroke-thickness bin
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# (K=30) resampled to a Zipf p*; a frozen CNN + deterministic thickness is the oracle (its
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# confusion matrix, recorded in the manifest, is the measurement-noise floor). Expect (per E1/E2):
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# the dry arm (g=0) collapses — rare modes die, forward-KL climbs, support shrinks — while a
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# grounded arm holds the tail. This is confirmation-only: SIGNS, not magnitudes (blueprint 3.5);
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# the exact synthetic oracle remains the anchor for every quantitative comparison. Falsifier: the
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# dry VAE shows no diversity loss, or grounding fails to arrest it.
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mnist:
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K: 30
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n_classes: 10
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style_bins: 3 # K = 10 classes x 3 stroke-thickness bins
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R: 1
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tail: zipf
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zipf_s: 1.5 # the rarest ~18/30 modes form a real tail (~9% of the mass)
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tail_threshold: 1.0e-2
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init: truth
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data_root: data
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model:
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kind: convvae
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latent: 32
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epochs: 30
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lr: 1.0e-3
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batch_size: 256
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beta: 1.0
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n_eval: 10000 # generate-and-classify samples for the mode-distribution readout
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oracle:
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epochs: 5 # frozen digit CNN (~98.5% mode accuracy = the noise floor)
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lr: 1.0e-3
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batch_size: 256
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cache: models/mnist_cnn.pt
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dynamics:
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n: 6000 # images the pupil VAE sees per generation (drift strength)
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grounding: {m: 0, policy: proportional} # m overwritten per g by the sweep
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generations: 15
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metrics:
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kl_floor: 1.0e-9
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support_eps: 1.0e-9
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sweep:
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- param: g
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values: [0.0, 0.1] # dry vs grounded (VAE collapse is strong; needs ~10% real, cf. grounding)
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output:
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dir: results/mnist_collapse
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