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>
110 lines
1.8 KiB
YAML
110 lines
1.8 KiB
YAML
experiment: mnist_collapse
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seed: 20260705
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n_replicates: 4
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source_config:
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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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mnist:
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K: 30
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n_classes: 10
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style_bins: 3
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R: 1
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tail: zipf
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zipf_s: 1.5
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tail_threshold: 0.01
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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: 0.001
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batch_size: 256
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beta: 1.0
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n_eval: 10000
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oracle:
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epochs: 5
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lr: 0.001
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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
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grounding:
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m: 0
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policy: proportional
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generations: 15
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metrics:
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kl_floor: 1.0e-09
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support_eps: 1.0e-09
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sweep:
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- param: g
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values:
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- 0.0
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- 0.1
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output:
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dir: results/mnist_collapse
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grid:
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- label:
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g: 0.0
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m: 0
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lineage_cfg:
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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
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R: 1
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tail: zipf
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zipf_s: 1.5
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tail_threshold: 0.01
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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: 0.001
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batch_size: 256
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beta: 1.0
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n_eval: 10000
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dynamics:
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n: 6000
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grounding:
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m: 0
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policy: proportional
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generations: 15
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metrics:
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kl_floor: 1.0e-09
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support_eps: 1.0e-09
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- label:
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g: 0.1
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m: 667
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lineage_cfg:
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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
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R: 1
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tail: zipf
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zipf_s: 1.5
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tail_threshold: 0.01
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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: 0.001
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batch_size: 256
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beta: 1.0
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n_eval: 10000
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dynamics:
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n: 6000
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grounding:
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m: 667
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policy: proportional
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generations: 15
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metrics:
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kl_floor: 1.0e-09
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support_eps: 1.0e-09
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