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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> |
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| .. | ||
| homecore-vs-home-assistant.md | ||
| mmfi-wifi-sensing-study.md | ||
| person-count-cog.md | ||
| pose-estimation-cog.md | ||
| wifi-pose-efficiency-frontier.md | ||