MTLoc: A Confidence-Based Source-Free Domain Adaptation Approach for Indoor Localization
Bibliographic record
Abstract
Indoor localization using radio-frequency identification (RFID) has benefited from deep learning, yet models trained in a labeled source environment often degrade when deployed in a different, unlabeled target environment. Unsupervised domain adaptation (UDA) aims to mitigate this distribution shift by aligning a source-trained model with target-domain data. In practice, the source dataset is frequently unavailable at adaptation time due to privacy and resource constraints. This motivates source-free domain adaptation (SFDA); however, most SFDA methods have been developed for classification, and extending them to indoor localization (regression) is challenging, especially when target datasets are small and noisy. Motivated by above limitations, we introduce MTLoc, a source-free mean-teacher approach for indoor localization. MTLoc includes a student and a teacher network: the student network is updated using noisy target data with teacher-generated pseudo-labels. The teacher network maintains stability through exponential moving averages. To further ensure robustness, we propose a correction mechanism in which the teacher’s pseudo-labels are refined using k-nearest neighbor correction. MTLoc allows for self-supervised learning on target data, facilitating effective adaptation to dynamic and noisy indoor environments. Validated using real-world data from our experimental setup with INLAN Inc., our results1show that MTLoc achieves high localization accuracy under challenging conditions, significantly reducing distance error compared to baselines. On average, it reduces MAE (d) by 20.0% on Cross and 22.5% on Square datasets. With confidence correction, these improvements reach 23.9% and 28.2% respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".