Multi-Source Domain Adaptation Using Ambient Sensor Data
Bibliographic record
Abstract
Smart buildings have gained increasing interest recently by providing several advanced solutions, especially AI-based solutions. Activity recognition and occupancy estimation are among the outcomes of smart buildings that can help provide several advantages such as energy management and security solutions. Previously, domain adaptation (DA) has been widely considered by researchers to transfer knowledge from source domains, where we have abundant labeled data, to a target domain where labeled data is scarce. It is a tedious and time-consuming task to label data, especially with smart building applications which is why researchers have considered unsupervised DA where we do have labeled data in the source domain and unlabeled data in the target domain. Semi-supervised DA (SSDA) adaptation has also been considered by researchers where we have a small amount of labeled data in the target domain. Most unsupervised DA (UDA) and SSDA methods transfer knowledge from one source to one target. However, it is possible to exploit knowledge from multiple source domains instead of one single domain to enhance the performance of the target domain. Multi-source DA (MSDA) is more difficult than single-source DA but also it is more efficient. In this research, we adapt several MDSA methods and evaluate them using sensorial datasets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".