Unsupervised Domain Adaptation for Estimating Occupancy and Recognizing Activities in Smart Buildings
Bibliographic record
Abstract
Activities Recognition (AR) and Occupancy Estimation (OE) are topics of current interest. AR and OE can develop many smart building applications such as energy management and can help \nprovide good services for residents. Prior research on AR and OE has typically focused on supervised machine learning methods. For a specific smart building domain, a model is trained using data collected from the current environment (domain). The created model will not generalize well when evaluated in a new related domain due to data distribution differences. Creating a model for \neach smart building environment is infeasible due to the lack of labeled data. Indeed, data collection is a tedious and time-consuming task. Unsupervised Domain Adaptation (UDA) is a good solution for the considered case. UDA solves the problem of the lack of labeled data in the target domain by allowing knowledge transfer across domains. In this research, we provide several UDA methods that mitigate the data distribution shift between source and target domains using unlabeled target data for OE and AR with and without direct access to labeled source data. Firstly, we consider \ntechniques that use only a trained source model instead of a huge amount of labeled source data to make domain adaptation. We adapted and tested several UDA methods such as Source HypOthesis Transfer (SHOT), Higher-Order Moment Matching (HoMM), and Source data Free Domain Adaptation (SFDA) on smart building data. Secondly, we adapt and develop several UDA methods that use labeled source data to estimate the number of occupants and recognize activities. The developed methods that have direct access to the source data are the Virtual Adversarial Domain Adaptation (VADA), Sliced Wasserstein Discrepancy (SWD), and Adaptive Feature Norm (AFN). Finally, we make a comparative analysis between several newly adapted deep UDA methods, applied to the tasks of AR and OE, with and without access to labeled source data.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".