# DAILOC: Domain-Incremental Learning for Indoor Localization using Smartphones

arXiv: https://arxiv.org/abs/2506.15554 · HTML: https://mlsanity.com/paper/2506.15554/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations](https://mlsanity.com/paper/1705.08841/) (2017)
- [An Efficient Domain-Incremental Learning Approach to Drive in All Weather Conditions](https://mlsanity.com/paper/2204.08817/) (2022)
- [Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization](https://mlsanity.com/paper/2205.08069/) (2022)
- [FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2307.01780/) (2023)
- [STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization](https://mlsanity.com/paper/2312.10312/) (2023)
- [SANGRIA: Stacked Autoencoder Neural Networks with Gradient Boosting for Indoor Localization](https://mlsanity.com/paper/2403.01348/) (2024)
- [Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method](https://mlsanity.com/paper/2406.16231/) (2024)

## Built on by (most cited first, 1 of 1)

- [Unified Class and Domain Incremental Learning with Mixture of Experts for Indoor Localization](https://mlsanity.com/paper/2511.17829/) (2025)

## Abstract (arXiv)

Wi-Fi fingerprinting-based indoor localization faces significant challenges in real-world deployments due to domain shifts arising from device heterogeneity and temporal variations within indoor environments. Existing approaches often address these issues independently, resulting in poor generalization and susceptibility to catastrophic forgetting over time. In this work, we propose DAILOC, a novel domain-incremental learning framework that jointly addresses both temporal and device-induced domain shifts. DAILOC introduces a novel disentanglement strategy that separates domain shifts from location-relevant features using a multi-level variational autoencoder. Additionally, we introduce a novel memory-guided class latent alignment mechanism to address the effects of catastrophic forgetting over time. Experiments across multiple smartphones, buildings, and time instances demonstrate that DAILOC significantly outperforms state-of-the-art methods, achieving up to 2.74x lower average error and 4.6x lower worst-case error.