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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?
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