Random frozen encoder + trained head matches a fully-trained encoder to
within 2-4pts (cross-subject <2pts). WiFi-CSI sensing is largely a
random-features + target-readout problem: barely a learned representation
to transfer, which unifies the zero-shot collapse, no-transfer results,
foundation-encoder failure, and why per-room calibration works. Practical:
invest in readout + calibration, not encoder pretraining.
Co-Authored-By: claude-flow <ruv@ruv.net>
Re-ran transfer on 14-class person-ID (harder than 6-activity HAR): same
null-transfer result (MM-Fi pretrain 91.7% = random 92.8%). Unified root
cause: CSI in-domain classification lives in the target-trained readout
(random projection already separable); learned reps don't transfer across
subjects/rooms/datasets. WiFi-CSI is distribution-locked. Addresses the
'HAR too easy' caveat.
Co-Authored-By: claude-flow <ruv@ruv.net>
Tested the cross-dataset frontier: MM-Fi-trained CSI representation does NOT
transfer beneficially to NTU-Fi HAR (frozen probe 91.5% = random features
93%; full fine-tune 75% < probe). CSI reps are distribution-locked, same
root cause as within-MM-Fi cross-subject/-env collapse. Caveat: NTU-Fi 6
coarse activities are an easy target (random->93%). Updates the study's
cross-dataset limitation from 'untested' to this measured result.
Co-Authored-By: claude-flow <ruv@ruv.net>
Consolidates the full campaign into one committed, citable artifact (the
detailed log was in a gitignored staging report): pose SOTA 83.6% + 20KB
int4 edge model; action recognition 88% (a WiFi task MM-Fi never
benchmarked); the generalization story (zero-shot collapse, few-shot
calibration rescue, task-general across pose+action); all honest negatives
(CORAL/DANN/instance-norm/SupCon/distillation/subject-scaling); the 11KB
calibration-adapter deployment recipe; honest limitations (cross-dataset
untested, ARM latency pending).
Co-Authored-By: claude-flow <ruv@ruv.net>
ruv