# IIns-VAE+: A Robust Transfer Learning Framework for Environmental Identification in Wireless Sensing

arXiv: https://arxiv.org/abs/2609.06131 · HTML: https://mlsanity.com/paper/2609.06131/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 54 references that are in the map.
- First posted on arXiv 5 Sep 2026.

## Builds on (most cited first, 19 of 19)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach](https://mlsanity.com/paper/1603.07080/) (2016)
- [Variational Inference using Implicit Distributions](https://mlsanity.com/paper/1702.08235/) (2017)
- [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2102.07572/) (2021)
- [6G White Paper on Localization and Sensing](https://mlsanity.com/paper/2006.01779/) (2020)
- [Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues](https://mlsanity.com/paper/2303.08106/) (2023)
- [Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications](https://mlsanity.com/paper/2009.05231/) (2020)
- [Minimax risk classifiers with 0-1 loss](https://mlsanity.com/paper/2201.06487/) (2022)
- [Double-Weighting for Covariate Shift Adaptation](https://mlsanity.com/paper/2305.08637/) (2023)
- [Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook](https://mlsanity.com/paper/2302.02107/) (2023)
- [CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach](https://mlsanity.com/paper/1806.07447/) (2018)
- [General Supervision via Probabilistic Transformations](https://mlsanity.com/paper/1901.08552/) (2019)
- [Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees](https://mlsanity.com/paper/2205.15942/) (2022)
- [A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks](https://mlsanity.com/paper/2205.08068/) (2022)
- [Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification](https://mlsanity.com/paper/2305.18206/) (2023)
- [Efficient Learning of Minimax Risk Classifiers in High Dimensions](https://mlsanity.com/paper/2306.06649/) (2023)
- [Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees](https://mlsanity.com/paper/2310.15974/) (2023)
- [ISAC: From Human to Environmental Sensing](https://mlsanity.com/paper/2507.13766/) (2025)
- [A Deep Learning Approach for Generating Soft Range Information from RF Data](https://mlsanity.com/paper/2305.13911/) (2023)

## Abstract (arXiv)

Environmental identification in wireless sensing is essential for 6G integrated sensing and communication (ISAC) systems to achieve reliable situational awareness. However, deep learning (DL) models for this task often fail to generalize under domain shift across diverse environments. While the Inter-Instance Variational Auto-encoder (IIns-VAE) learns features of rich representation, its neural classifier remains vulnerable to these distribution changes. In this paper, we propose IIns-VAE+, a hybrid model that combines the IIns-VAE framework with Minimax Risk Classifiers (MRC) to improve adaptability in transfer learning scenarios. We use real-world datasets to evaluate our framework across three transfer learning scenarios, including general to specific room environments, high to low label resolutions, and mixed to specific environments. The experimental results indicate that IIns-VAE+ significantly outperforms baselines, demonstrating its critical value in building adaptable and robust perceptive networks in future 6G systems.