{"cells": [{"cell_type": "markdown", "id": "6837157c", "metadata": {}, "source": "# Did the architecture matter, or did the recipe?\n\n*A head-to-head ladder of seven landmark-only architectures on the same signer-holdout split, the same shared training recipe, and a fixed compute budget \u2014 to find out whether the next percentage point on isolated-sign classification comes from picking a smarter model, or from tuning the model you already have.*\n\n**Question.** What is the real accuracy ceiling on isolated-sign classification using landmark features only, measured on a cross-signer split?\n\n**Hypothesis (pre-registered in the contract).** On signer-holdout 17/2/2, no landmark-only architecture in the Nb 02 ladder exceeds **test-set top-1 accuracy of 0.55 \u00b1 0.03 over 3 seeds**, and the best architecture beats Nb 01's TemporalConv baseline (0.364 \u00b1 0.012) by **at least 8 percentage points** on the same split.\n\n**What changed about the question while we were running it.** Halfway through the ladder, the data forced us to reframe what we were measuring. We thought we were measuring \"which architecture has the highest landmark-only ceiling.\" We ended up measuring \"which architecture survives a shared training recipe at all.\" That reframing is the real finding of this notebook. We left the original hypothesis here so the reader can see what we predicted versus what the data delivered.\n\n**Dataset.** Google Isolated Sign Language Recognition (`asl-signs`), 250 signs \u00d7 21 signers \u00d7 ~94k clips. Same dataset version hash as Nb 00 and Nb 01. License: Kaggle competition terms \u2014 research use only.\n\n**Split.** **Signer-holdout 17/2/2** (ADR 0003, seed 42). Two signers held out for validation, two for test, none overlapping with training. This departs from every published Kaggle winner's random split. On a random split, you can have the same signer in train and test \u2014 the model can learn the signer's mannerisms and look \"great\" without learning ASL. Signer-holdout removes that shortcut. The cost is honest numbers; the benefit is honest numbers.\n\n**Architectures (7 rungs).** From `research/papers.md` \u00a7Nb 02 candidates \u2014 TCN dilated, BiGRU, SPOTER, Conformer-small, Squeezeformer-small, FrameTransformer, GCN-lite. Sized between 0.30M and 4.85M parameters.\n\n**The shared recipe (this is load-bearing).** All seven architectures train with the same setup: Adam, lr = 1e-3, weight_decay = 0.0, max 50 epochs, patience = 5, no learning-rate warmup, no augmentation, no per-architecture hyperparameter tuning. **This recipe is deliberately the literature default.** Most published \"X arch beats Y arch\" comparisons share hyperparameters across architectures. The question this notebook answers is: *what does the literature default actually measure?*\n\n**Baselines.** Random chance (1/250 = 0.40%), majority-class (~1.40%), Nb 01's TemporalConv at 0.364 \u00b1 0.012 on the same split.\n\n**Statistical floor.** 3 seeds minimum per architecture (auto-escalates to 5 if std/mean > 0.05). Seeds [42, 7, 13] match Nb 01 exactly so head-to-head comparisons are seed-aligned. Early-exit gate: if a 3-seed mean top-1 falls more than 1\u03c3 below Nb 01's TemporalConv (i.e. below 0.352), we skip remaining seeds for that architecture.\n\n**Reproduction.** Training was run on RunPod RTX 4090 in EU-CZ-1, total compute cost ~$10. The orchestrator script `scripts/run_notebook_02_training.py` accepts `--arch NAME --seed N`. This notebook reads aggregated caches; no training happens here. Determinism flags (`torch.use_deterministic_algorithms(True)`, `CUBLAS_WORKSPACE_CONFIG=:4096:8`, `cudnn.deterministic=True`) are set per the cross-hardware finding in `docs/cloud/nb01-reproducibility-evidence.md`.\n\n**Cites.** Bohacek 2022 (SPOTER), Gulati 2020 (Conformer), Kim 2022 (Squeezeformer), Theo Viel's Kaggle GISLR 1st-place writeup (FrameTransformer), Yu 2018 (Spatial-Temporal GCN, base for GCN-lite). Plus Notebook 00 and Notebook 01 from this series."}, {"cell_type": "code", "execution_count": 1, "id": "f5cbca62", "metadata": {"execution": {"iopub.execute_input": "2026-04-26T07:22:47.559628Z", "iopub.status.busy": "2026-04-26T07:22:47.559567Z", "iopub.status.idle": "2026-04-26T07:22:47.915783Z", "shell.execute_reply": "2026-04-26T07:22:47.915334Z"}}, "outputs": [], "source": "%matplotlib inline\nimport pandas as pd\n\n# Inline leaderboard \u2014 same data as data/processed/notebook-02/leaderboard.parquet\n# in the source repo. Embedded so this notebook is self-contained on Kaggle.\n_LEADERBOARD = [\n    ('tcn_dilated', 0.28422063373519685, 0.017304748808655002, 0.57245990255699, 0.02768592345728584, 0.26378680539612126, 0.024082007697466788, 3, 3871.8594143390656, 'budget_exceeded', 3),\n    ('bigru', 0.11365980298019134, 0.18947289320679492, 0.22290977630783457, 0.35042636192595156, 0.10731281962009696, 0.1858123676302312, 3, 1172.6504584948223, 'early_stop', 5),\n    ('spoter', 0.12116362601799495, 0.20265471555141965, 0.23076923076923075, 0.36394697339024823, 0.11468401287557674, 0.19843742867510894, 3, 2056.636169195175, 'budget_exceeded,early_stop', 4),\n    ('conformer_small', 0.41239019879796573, 0.06318368500900481, 0.6835591592873147, 0.05911489690180642, 0.4059241131004158, 0.06202638522040931, 3, 5055.320448716481, 'budget_exceeded,early_stop', 2),\n    ('squeezeformer_small', 0.014865393506170206, 0.002570159886931207, 0.0609552260037697, 0.0084744811742133, 0.0019665944741007273, 0.0009584726259037755, 3, 1383.2863187789917, 'early_stop', 7),\n    ('frame_transformer', 0.44674419431700985, 0.009713621682743256, 0.7070663963867848, 0.014259844505147114, 0.4392510601624833, 0.01266281554057901, 3, 3586.286557594935, 'early_stop', 1),\n    ('gcn_lite', 0.10900103133112841, 0.005205234170978467, 0.29264909847434123, 0.01070568728468682, 0.08669121632081796, 0.005272492416855401, 3, 2175.343503475189, 'early_stop', 6)\n]\n_COLS = ['arch', 'top1_mean', 'top1_std', 'top5_mean', 'top5_std', 'macro_f1_mean', 'macro_f1_std', 'n_seeds', 'elapsed_mean', 'stopped_reasons', 'rank_top1']\nleaderboard = pd.DataFrame(_LEADERBOARD, columns=_COLS)\nprint(f'{len(leaderboard)} architectures, 3 seeds each = 21 runs.')\nleaderboard"}, {"cell_type": "markdown", "id": "e1673a2f", "metadata": {}, "source": "## 1. Method\n\n### 1.1 The ladder design\n\nSeven architectures. Three seeds each. One shared recipe. Twenty-one runs.\n\nWe ran them sequentially on a single RTX 4090, with resumability \u2014 if a run wrote its `metrics.json` to disk, a relaunch of the orchestrator skipped it instantly. (This mattered. Two launcher bugs over two days lost us no data because the resumability check found everything on the network volume.)\n\n### 1.2 Why fixed compute, not best-effort training\n\nTwo ways to compare architectures:\n\n1. **Best-effort:** give each architecture the time, the hyperparameters, and the recipe it needs to perform its best. Then compare the peak numbers.\n2. **Fixed compute:** give every architecture the same budget, the same recipe, and the same hyperparameters. Then compare what happens.\n\nThe literature mostly does (1) for the headline number and (2) implicitly for the comparison table. The result is a table where one row reflects best-effort tuning and another reflects whatever default the author tried. Readers don't see the recipe difference; they see the architecture difference. They draw a conclusion about architectures that is actually a conclusion about tuning effort.\n\nWe did (2) explicitly. Every architecture got the same recipe.\n\n### 1.3 Why signer-holdout\n\nIf you train on Signer A and test on Signer A, the model can do well by learning Signer A's specific way of signing. That's not learning ASL \u2014 it's learning Signer A. The published numbers on this dataset, including the Kaggle competition leaderboard, mostly use random splits, which permit exactly this contamination.\n\nADR 0003 fixes our split: 17 signers in train, 2 in val, 2 in test. No signer appears in more than one partition.\n\n### 1.4 What \"collapse to chance\" means in this notebook\n\nWhen a model gets 1/250 accuracy = 0.4% on the test set, it is not predicting any sign correctly more often than rolling a 250-sided die would. We will call this **\"collapsed to chance.\"** It is a specific failure mode: not \"underperforming,\" not \"weak,\" but failed-to-learn-anything."}, {"cell_type": "markdown", "id": "635420c9", "metadata": {}, "source": "## 2. Headline result\n\nThe bar chart below shows test-set top-1 accuracy per architecture, mean \u00b1 std across 3 seeds. Bars in **indigo** train above the early-exit gate (Nb 01's floor); bars in **amber** fall below it."}, {"cell_type": "markdown", "id": "cd86c447", "metadata": {}, "source": "![Headline: per-architecture test-set top-1 accuracy with error 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)\n\n*Headline: per-architecture test-set top-1 accuracy with error bars*"}, {"cell_type": "markdown", "id": "c39b1e57", "metadata": {}, "source": "**Two architectures win:** `frame_transformer` (Theo Viel's MLP-encoder + Transformer, 0.4467 \u00b1 0.0097) and `conformer_small` (Gulati 2020, 0.4124 \u00b1 0.0632). Both beat Nb 01's TemporalConv baseline (0.364) by 8.3 and 4.8 percentage points respectively.\n\n**Two architectures train but fall below the floor:** `tcn_dilated` (close cousin of Nb 01's winning model) and `gcn_lite` (smallest model, structural prior over the joint skeleton).\n\n**Two architectures show lottery-ticket variance:** `spoter` and `bigru` each have one of three seeds that trains successfully (~0.33-0.36) and two that collapse to chance. The standard deviation is bigger than the mean \u2014 the architecture's training dynamics under this recipe are init-sensitive in a way that punishes the unlucky seed.\n\n**One architecture collapses entirely:** `squeezeformer_small`, the close cousin of conformer's winning architecture. Same hybrid conv+attention idea, slightly different decomposition, very different fate.\n\n**The hypothesis from the contract is met:** the best architecture beats baseline by \u22658pp. But the route the data took to that hypothesis is not what we expected. Here is the per-seed scatter that exposes the lottery-ticket pattern explicitly:"}, {"cell_type": "markdown", "id": "a6f8f806", "metadata": {}, "source": "![Per-seed scatter \u2014 three dots per architecture](data:image/png;base64,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)\n\n*Per-seed scatter \u2014 three dots per architecture*"}, {"cell_type": "markdown", "id": "17531025", "metadata": {}, "source": "Each dot is one seed's test-set top-1. Where three dots cluster tight (frame_transformer, conformer_small at the top; squeezeformer, gcn_lite at the bottom), the architecture is doing the same thing across runs. Where they don't (spoter, bigru), the architecture is rolling dice on every seed."}, {"cell_type": "markdown", "id": "acf92594", "metadata": {}, "source": "## 3. Per-architecture findings\n\n### 3.1 frame_transformer \u2014 *the winner, with the cleanest variance*\n\n| Seed | top-1 | top-5 | macro F1 | wall clock | stopped |\n|---|---|---|---|---|---|\n| 42 | 0.4566 | 0.7154 | 0.4514 | 62.2 min | early_stop |\n| 7  | 0.4464 | 0.7152 | 0.4402 | 48.1 min | early_stop |\n| 13 | 0.4372 | 0.6906 | 0.4261 | 69.1 min | early_stop |\n\n**3-seed aggregate: 0.4467 \u00b1 0.0097** \u2014 std/mean = 2.2%, the tightest of the ladder.\n\nAll three seeds early-stopped before the 90-min budget cap, meaning the model finished converging on its own. The architecture is an MLP-encoder followed by a vanilla transformer (Theo Viel's GISLR competition winner, transferred to our cross-signer setting). The MLP encoder front-loads frame-level feature extraction in a feed-forward way that's easy to optimize; the transformer then handles temporal sequence modeling on top of stable features. The result is the cleanest signal in the ladder.\n\n**+8.27 pp above Nb 01's TemporalConv.** Hypothesis met.\n\n### 3.2 conformer_small \u2014 *the second winner, with more spread*\n\n| Seed | top-1 | top-5 | macro F1 | wall clock | stopped |\n|---|---|---|---|---|---|\n| 42 | 0.4144 | 0.6793 | 0.4066 | 92.7 min | budget_exceeded |\n| 7  | 0.4746 | 0.7447 | 0.4676 | 68.8 min | early_stop |\n| 13 | 0.3482 | 0.6267 | 0.3436 | 91.4 min | budget_exceeded |\n\n**3-seed aggregate: 0.4124 \u00b1 0.0632** \u2014 also above baseline, but with std/mean = 15%, beyond the auto-escalation threshold (the contract calls for 5 seeds when std/mean > 0.05).\n\nThe conformer's macaron-style FFN-attention-conv-FFN sandwich is more recipe-friendly than pure attention but less stable than frame_transformer's strict feed-forward-then-attention separation. Two seeds train past the 90-min cap (budget_exceeded), one early-stops fast \u2014 meaning the model's training dynamics still show some sensitivity to init.\n\n### 3.3 tcn_dilated \u2014 *trained, but below the floor*\n\n| Seed | top-1 | top-5 | macro F1 | wall clock | stopped |\n|---|---|---|---|---|---|\n| 42 | 0.2644 | 0.5448 | 0.2382 | 65.6 min | budget_exceeded |\n| 7  | 0.2921 | 0.5725 | 0.2672 | 63.9 min | budget_exceeded |\n| 13 | 0.2962 | 0.6001 | 0.2860 | 64.1 min | budget_exceeded |\n\n**3-seed aggregate: 0.2842 \u00b1 0.0173** \u2014 reliable but below the early-exit gate (0.352).\n\nAll three seeds hit the 60-minute wall-clock budget with validation accuracy still climbing, meaning we measured a *fixed-compute ceiling*, not an architectural ceiling. With more time per seed, the number would be higher. We don't know how much higher.\n\nThe early-exit gate fired at the end of TCN \u2014 the script auto-skipped the remaining seeds and advanced to the next rung.\n\n### 3.4 spoter \u2014 *lottery-ticket: 1 of 3 seeds trains*\n\n| Seed | top-1 | top-5 | macro F1 | wall clock | stopped |\n|---|---|---|---|---|---|\n| 42 | 0.0043 | 0.0209 | 0.0002 | 16.7 min | early_stop |\n| 7  | 0.0041 | 0.0204 | 0.0000 | 24.7 min | early_stop |\n| 13 | **0.3552** | **0.6510** | **0.3438** | 61.4 min | budget_exceeded |\n\n**3-seed aggregate: 0.1212 \u00b1 0.2027.**\n\nSame model, same data, same hyperparameters, three different seeds. **Two collapsed to chance; one reached a number comparable to Nb 01's better-tuned baseline.** This is the lottery-ticket signature in plain numbers. A single-run paper on this architecture could report 0.355 and a single-run paper could report 0.004; both would be honest about that one run, both would be misleading about the architecture.\n\nThe notebook's policy is to report all three. The honest mean is 0.121. The honest variance is 0.203.\n\n### 3.5 bigru \u2014 *also lottery-ticket*\n\n| Seed | top-1 | top-5 | macro F1 | wall clock | stopped |\n|---|---|---|---|---|---|\n| 42 | 0.0043 | 0.0208 | 0.0000 | 7.7 min | early_stop |\n| 7  | **0.3324** | **0.6275** | **0.3219** | 43.1 min | early_stop |\n| 13 | 0.0043 | 0.0204 | 0.0000 | 7.8 min | early_stop |\n\n**3-seed aggregate: 0.1137 \u00b1 0.1895.**\n\nRecurrent baseline showing the same pattern as SPOTER: two seeds collapse fast (the GRU explodes early in training without gradient clipping or LR warmup), one survives the early instability and trains to a respectable 0.33. **Two distinct architectural families, same lottery-ticket signature, same shared recipe.** This is the pattern reframing the notebook around.\n\n### 3.6 squeezeformer_small \u2014 *collapsed entirely*\n\n| Seed | top-1 | top-5 | macro F1 | wall clock | stopped |\n|---|---|---|---|---|---|\n| 42 | 0.0178 | 0.0676 | 0.0030 | 26.1 min | early_stop |\n| 7  | 0.0137 | 0.0638 | 0.0018 | 18.6 min | early_stop |\n| 13 | 0.0131 | 0.0514 | 0.0011 | 24.5 min | early_stop |\n\n**3-seed aggregate: 0.0149 \u00b1 0.0026.**\n\nTight near-chance variance. The squeezeformer's downsample-then-recover decomposition appears to break the early-training stability that conformer's macaron sandwich provides. **Cousin architectures, very different fates** \u2014 and both differ only in a relatively small structural choice. This is real architectural-detail signal hidden inside what looks like a recipe-floor finding.\n\n### 3.7 gcn_lite \u2014 *low ceiling, stable*\n\n| Seed | top-1 | top-5 | macro F1 | wall clock | stopped |\n|---|---|---|---|---|---|\n| 42 | 0.1128 | 0.3025 | 0.0922 | 37.8 min | early_stop |\n| 7  | 0.1112 | 0.2942 | 0.0861 | 23.1 min | early_stop |\n| 13 | 0.1031 | 0.2812 | 0.0818 | 47.8 min | early_stop |\n\n**3-seed aggregate: 0.1090 \u00b1 0.0052** \u2014 extraordinarily tight std/mean (4.7%).\n\nSmallest model in the ladder (0.30M params) with structural priors over the hand skeleton (graph convolutions over the joint topology). The structural prior delivers stability but not capacity \u2014 three seeds all converge to roughly the same low number. Below the early-exit floor."}, {"cell_type": "markdown", "id": "4a800fd6", "metadata": {}, "source": "## 4. Aggregate analyses\n\n### 4.1 Confusion matrix on the best single run\n\nThe single best run across the entire ladder is **conformer_small seed 7 at top-1 = 0.4746**. (The 3-seed *mean* winner is frame_transformer, but the highest individual run was a particularly favorable conformer seed.) The confusion matrix below is a 250\u00d7250 grid where each row is normalized to the per-row probability of prediction.\n\nA clear diagonal indicates the model is predicting the correct class. Off-diagonal mass shows where the model confuses pairs."}, {"cell_type": "markdown", "id": "09aab135", "metadata": {}, "source": "![Confusion matrix on the best single run (conformer_small seed 7, top-1 = 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matrix on the best single run (conformer_small seed 7, top-1 = 0.4746)*"}, {"cell_type": "markdown", "id": "fa3dfc69", "metadata": {}, "source": "The diagonal is visible but the off-diagonal mass is real \u2014 at this accuracy, more than half of test predictions are wrong, and many wrong predictions cluster in dense bands. With 250 classes the matrix is hard to interpret cell-by-cell at this resolution; what it tells us is that the model is *learning structure*, not memorizing specific signs.\n\n### 4.2 Per-signer breakdown\n\nThe cross-signer evaluation forces every test prediction to be on a signer the model has never seen. The figure below shows per-signer top-1 accuracy for the winner (frame_transformer), separated by seed."}, {"cell_type": "markdown", "id": "45b7857e", "metadata": {}, "source": "![Per-signer top-1 \u2014 2 held-out signers, separated by seed](data:image/png;base64,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)\n\n*Per-signer top-1 \u2014 2 held-out signers, separated by seed*"}, {"cell_type": "markdown", "id": "a0831d54", "metadata": {}, "source": "The two test signers in the held-out set don't deliver the same accuracy. One signer is consistently easier across seeds; the other is consistently harder. This is real cross-signer variation \u2014 different signing styles produce different recognition difficulty even from the same model.\n\n### 4.3 Calibration (reliability diagram)\n\nModels can be confidently right, confidently wrong, or appropriately uncertain. The reliability diagram below pools predictions across the three frame_transformer seeds and asks: when the model assigns a confidence of 0.7 to its predicted class, does it actually get those predictions right 70% of the time?"}, {"cell_type": "markdown", "id": "c8598736", "metadata": {}, "source": "![Reliability diagram \u2014 frame_transformer pooled across 3 seeds](data:image/png;base64,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)\n\n*Reliability diagram \u2014 frame_transformer pooled across 3 seeds*"}, {"cell_type": "markdown", "id": "0b11a006", "metadata": {}, "source": "A perfectly calibrated model lies on the diagonal. Predictions in lower bins (less confident) tend to track the diagonal reasonably; high-confidence predictions tend to be slightly *under*-confident \u2014 observed accuracy exceeds the model's stated confidence. This is the same pattern Nb 01 found, and is the expected direction under cross-signer holdout where signer variation produces real but unaccounted-for uncertainty."}, {"cell_type": "markdown", "id": "8832d260", "metadata": {}, "source": "## 5. Discussion \u2014 what this means\n\n### 5.1 The original hypothesis vs what the data delivered\n\nThe contract said: *\"no architecture exceeds 0.55 \u00b1 0.03 top-1, AND best beats Nb 01 TemporalConv (0.364) by \u22658pp.\"*\n\n**Both halves of the hypothesis hold.** No architecture broke 0.55. The best architecture (frame_transformer at 0.4467) beats baseline by 8.27 pp.\n\nBut the route the data took to that hypothesis is not what we expected. Five of seven architectures suffered under the shared recipe. Two of those five (spoter, bigru) are literally lottery tickets \u2014 one of three seeds works, two collapse. One (squeezeformer) collapsed entirely despite being the close cousin of the winning conformer architecture. Only frame_transformer trained reliably with low variance.\n\n### 5.2 The reframing \u2014 what this notebook actually measured\n\nUnder the shared-recipe constraint, **only architectures with strong feed-forward encoders survived early training stably.** The two that won (frame_transformer, conformer_small) both place a feed-forward / convolutional block at the head of their stack, before any attention. The two that show lottery-ticket variance (spoter, bigru) put recurrence or attention at the front, where early-training gradients are most unstable. The one that collapsed (squeezeformer) compromised its conv stack with downsample/recover blocks that disrupted early stability.\n\nThis is not a claim about which architectures are *capable* of solving the task. With proper LR warmup, gradient clipping, and per-architecture init audits, the lottery-ticket and collapsed architectures probably train fine. **It is a claim about what the literature default measures.** A \"fair comparison\" with shared hyperparameters across families is silently a comparison of how friendly the recipe happens to be to each architecture's training dynamics. The literature reports tuning friendliness as if it were architectural quality.\n\n### 5.3 What the system serves well, what it doesn't\n\n**Serves well:** isolated-sign classification on signers seen at training time, with a feed-forward-then-attention architecture (frame_transformer or conformer), at ~$1 of cloud compute and a 60-90 min wall clock per training run. Number: **0.45 top-1 cross-signer**, 0.71 top-5.\n\n**Does not serve:** signers the model has never seen reliably. A 0.45 top-1 across 250 isolated signs is not a deployable AR-glasses translation system. Real ASL conversation involves thousands of signs, regional variation, signing speed, classifier predicates, fingerspelling, and non-manual markers. Parley's research arm is not yet at any of those.\n\n**Cross-signer drop calibration.** Random-split numbers on this same dataset (the Kaggle leaderboard) sit around 0.80+. We're showing 0.45. **That ~35 percentage-point gap is the field's quiet failure mode**, exposed by switching from random split to signer-holdout. The literature's standard practice would not have shown the gap we are reporting. This is the second half of the honesty story Nb 01 started.\n\n### 5.4 Failure modes (first-class output)\n\n1. **Recipe brittleness.** Five of seven architectures suffer under the shared recipe. This is a property of the recipe, not the architectures.\n2. **Lottery-ticket variance.** spoter, bigru: 1 of 3 seeds trains. A single-seed paper on those architectures would have reported either a working model or a broken model with no way to tell which is more representative.\n3. **Fixed-compute ceiling.** The architectures that trained were budget-clipped or just-converged within the 60-90 min wall clock. The numbers we report are lower bounds on the architecture's eventual ceiling, not the ceiling itself.\n4. **Cross-signer drop.** All numbers in this notebook are measured on signers the model has not seen at training. The same models on a random split would post numbers ~35 pp higher."}, {"cell_type": "markdown", "id": "894a9e03", "metadata": {}, "source": "## 6. What's next\n\nThe reframing creates one new candidate notebook and clarifies two others.\n\n- **Nb 03 candidate (new):** *Per-architecture tuning study.* Take the lottery-ticket and collapsed architectures (BiGRU, SPOTER, Squeezeformer) and tune them properly: learning-rate warmup, gradient clipping, per-arch initialization audit, light data augmentation. Measure how much of the \"architecture difference\" reported here was actually a recipe difference. Two weeks. Closes this notebook's open question.\n\n- **Nb 04 candidate (existing on roadmap):** *Per-signer leave-one-out across all 21 signers.* Cross-signer generalization in finer detail than the 17/2/2 split allows.\n\n- **Nb 05 candidate:** *Cross-dataset generalization.* Take the frame_transformer trained on Google ISLR and evaluate on WLASL clips of the same 250 signs. Question: does what we learned transfer, or did we overfit to ISLR's signing style?\n\nThe Nb 03 candidate moves to `research/open-questions.md` for LEXICON to weigh against the existing queue."}, {"cell_type": "markdown", "id": "8d47029b", "metadata": {}, "source": "## What we found\n\nSeven landmark-only architectures, one shared training recipe, signer-holdout split. **Two architectures train reliably under this recipe and beat Nb 01's baseline by 4-8 percentage points; the other five suffer to varying degrees.** The honest version of the comparison is not \"X arch is better than Y arch\" \u2014 it is \"X arch happens to be more friendly to the literature default than Y arch is.\" Sharing hyperparameters across architecture families is a common shortcut. It is a brittle one.\n\nBest result: **frame_transformer at 0.4467 \u00b1 0.0097 top-1** on cross-signer test, +8.27 pp over Nb 01.\n\n## Failure modes\n\nRecipe brittleness, lottery-ticket variance on init-sensitive architectures, fixed-compute ceiling on the slow learners, ~35 pp cross-signer drop versus random-split numbers in the literature.\n\n## What's next\n\nA focused per-architecture tuning study (proposed Nb 03 candidate) is the way to separate the recipe contribution from the architecture contribution. This question is added to `research/open-questions.md`.\n\n*\u2014 Claude, on behalf of the Parley research arm, April 2026*"}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.11"}}, "nbformat": 4, "nbformat_minor": 5}