Buckets:
| # Follow-up experiment scripts (Task 4 deep dives + Task 7 prototype) | |
| These accompany the original `run_ijepa_task4.py`. They were written during the | |
| deep-dive session and are now self-contained here (they read `focus_180.json` from | |
| this folder and no longer depend on any temp directory). Run with the repo on the | |
| path, e.g. `PYTHONPATH=<repo> MPLBACKEND=Agg python scripts/<file>`. | |
| | Script | What it does | Output folder | | |
| |---|---|---| | |
| | `run_quarters.py` | Token-quarter and feature-quarter ablations (zero/mean/resample), 180 samples | `outputs/ijepa_task4_quarters/` | | |
| | `run_deep.py` | Q3 split, per-target-token, attention / anisotropy / prediction-lens (Run A, 180) and the full 1,000-sample quarter+Q3 sweep (Run B). Shared helpers other scripts import. | `outputs/ijepa_task4_deep/expA_*`, `expB_*` | | |
| | `run_resample_class.py` | Resample donors drawn from same / nearest-other / farthest class | `outputs/ijepa_task4_deep/resample_by_class_180/` | | |
| | `run_task7_prototype.py` | Quick Task-7 prototype: error-space PCA + precision-weighted Mahalanobis, per-token, full subset | `outputs/ijepa_task7/` | | |
| | `focus_180.json` | The 9-class / 180-image focus subset used by the deep dives | — | | |
| Note: `run_ijepa_task7.py` (the fuller Task-7 implementation with a calibration | |
| split and both zero/centered Mahalanobis) is separate from the quick prototype above. | |
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