| schema_version: 1 |
| protocol_id: imagenet-e4-alignment-single-seed-20260716 |
| title: Matched ImageNet gate-feature alignment retraining |
| registered_at_utc: "2026-07-16T19:00:00Z" |
| relationship_to_prior_work: >- |
| Post-hoc, single-seed exploratory mechanism follow-up. These tasks do not |
| enter the ImageNet-v2 Holm family and do not provide seed-level inference. |
| known_before_registration: |
| paper_recipe_relu6_top1: 78.746 |
| relu6_only_top1: 78.122 |
| no_gate_top1: 14.376 |
| learned_smooth_top1: 78.670 |
| fixed_c6_top1: 78.798 |
| release_recipe_paper_bn_top1: 79.158 |
| frozen_interventions_are_distribution_shifted: true |
| claim_restrictions: |
| - Report fixed final checkpoints even when the direction is unfavorable. |
| - The historical baseline has retrospective-unverified source provenance. |
| - Effects at or below 0.3 percentage points are descriptive near-baseline results. |
| - Effects above 1.0 percentage point are material single-seed signals, not significance claims. |
| - High post-training channel coherence indicates adaptation, not maintained misalignment. |
|
|
| data: |
| runtime_root: /tmp/gmnet_data/imagenet-1k |
| source_archive_uri: s3://snap-research-cv-code/ywang29/datasets/imagenet-1k/imagenet-1k.tar |
| expected_archive_bytes: 161381969920 |
| canonical_manifest_sha256: bb70bc9f530db6bb24f70e648624b35281bfbc76a57775d589a2e0209dd98661 |
| train_sample_index_sha256: e74104e28ef79fff114e729e81d050a7b4f0c294c4d82345bad13c320d7255b2 |
| val_sample_index_sha256: 5ed16b5cd3ebd64e90cd7a8380b4e19d4c0d82eedcca3489dfa29313ce88d6f6 |
| train_sampled_content_sha256: dea27b5dbcb5f110ed47c075d473ebd0f6aefc446b45ab1e62d8246f2a153798 |
| val_sampled_content_sha256: d80a57419bc9396df7c01e29a2b17698c02c3a9a68dc4006a1fe87ef129ed166 |
| expected_train_samples: 1281167 |
| expected_val_samples: 50000 |
|
|
| run_root: /nfs/ywang29/GmNet/runs/e4_alignment |
| deploy_root: /nfs/ywang29/GmNet/depoly/e4_alignment_20260716 |
| code_manifest_path: configs/code_manifests/e4_alignment_20260716.json |
| smoke_evidence_path: /nfs/ywang29/GmNet/depoly/e4_alignment_20260716/smoke_evidence.json |
| policy: |
| seed: 0 |
| seed_replication_in_scope: false |
| model_variant: s3 |
| baseline_recipe: paper-supplementary-table8-v1 |
| baseline_top1: 78.746 |
| intervention_seed: 41041 |
| block_seed_stride: 10007 |
| gpus_per_job: 8 |
| epochs: 300 |
| eta_class: greater_than_12h |
| checkpoint_policy: fixed_last |
| resume: auto |
| post_eval: strict_official |
| approval_marker_required_for_every_task: true |
| approval_requires_8gpu_strict_resume_smoke: true |
| output_lock: nonblocking_flock |
| launchjob_submitted_by_generator: false |
|
|
| historical_baseline: |
| evidence_id: legacy_relu6_s3_seed0 |
| acceptance: accepted_historical_seed0_alias |
| code_provenance: retrospective_unverified |
| run_dir: /nfs/ywang29/GmNet/runs/e0_s3_seed0 |
| target_config: configs/e0_baseline/imagenet_gmnet_s3.yaml |
| semantic_projection_sha256: 63c040674fd48ef97272392a4fb52e46534f291a43a979c1077a1ac0904deddd |
| expected: |
| checkpoint_last.pt: e03401ab71852656b7e62d82376eb628efdcc158d310088afd39e0e6b98d1930 |
| config_source.yaml: 0460961aa6de4c1127d03abafbe30b24178d90e6ca5d8b84d4201d1ccbab6a64 |
| config_resolved.yaml: d669c6c6f57815b9e3ead42a99c160ba399ef15df40fdd845f899c3b97d396d5 |
| data_manifest.json: e130879b003a8b4f6630afc9e2cf606789cb722802df742e046ac538e183ce40 |
| official_eval/checks.json: 7b1010999d913ab5e4ae267279cfab2d6172b60b9d4b29402a2b374a40145f84 |
| official_eval/results.json: 614bf87d7fd9f63ef2512c121e8a452ab85ea6b06f4a9237e7258db7dbd6129c |
| official_eval/per_sample.npz: 3f719366080c2d510efff6bf318201dc5ebd20fa37f5bc16947efabff26e0f31 |
| official: |
| run_name: e0_s3_seed0 |
| gate_type: relu6_self |
| top1: 78.746 |
| checkpoint_config_sha256: a914c8e570ffb0cc953afff81c49dd76bd5b606beca95fdbd2c9445119c647c5 |
| artifacts_sha256: 33a79b915eedbbf45c2e31ccf69595a23685b757fcb66ab080281132e729e6be |
| parameter_count: 7791544 |
| state_tensor_count: 365 |
| model_state_schema_sha256: 352cb771eaf4123fb9364dfd25d0151f9c2e6fa5493b989759091819f8981c99 |
|
|
| tasks: |
| - task_id: e4a_s3_stop_gradient_seed0 |
| experiment: E4 |
| model: s3 |
| gate: relu6_self |
| gate_intervention: stop_gradient |
| gate_intervention_seed: 41041 |
| seed: 0 |
| config_path: configs/e4_alignment/imagenet_gmnet_s3_stop_gradient.yaml |
| role: matched_alignment_retraining |
| status: ready_after_smoke |
| contrast: stop-gradient minus historical aligned ReLU6 self-gate |
| question: Does the derivative through the gate branch materially aid optimization? |
|
|
| - task_id: e4a_s3_channel_derangement_seed0 |
| experiment: E4 |
| model: s3 |
| gate: relu6_self |
| gate_intervention: channel_derangement |
| gate_intervention_seed: 41041 |
| seed: 0 |
| config_path: configs/e4_alignment/imagenet_gmnet_s3_channel_derangement.yaml |
| role: matched_alignment_retraining |
| status: ready_after_smoke |
| contrast: channel-deranged minus historical aligned ReLU6 self-gate |
| question: Can the network retain accuracy when feature and gate channels are persistently mismatched? |
|
|
