# Adversarial Learning Networks: Source-free Unsupervised Domain Incremental Learning

arXiv: https://arxiv.org/abs/2301.12054 · HTML: https://mlsanity.com/paper/2301.12054/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 28 Jan 2023.

## Builds on (most cited first, 16 of 16)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Generalized Source-free Domain Adaptation](https://mlsanity.com/paper/2108.01614/) (2021)
- [Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease](https://mlsanity.com/paper/1806.01738/) (2018)
- [Domain Adaptation without Source Data](https://mlsanity.com/paper/2007.01524/) (2020)
- [Multi-source Distilling Domain Adaptation](https://mlsanity.com/paper/1911.11554/) (2019)
- [Incremental Adversarial Domain Adaptation for Continually Changing Environments](https://mlsanity.com/paper/1712.07436/) (2017)
- [Class-Incremental Domain Adaptation](https://mlsanity.com/paper/2008.01389/) (2020)
- [Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift](https://mlsanity.com/paper/2006.14988/) (2020)
- [Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition](https://mlsanity.com/paper/2103.08637/) (2021)

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

- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)

## Abstract (arXiv)

This work presents an approach for incrementally updating deep neural network (DNN) models in a non-stationary environment. DNN models are sensitive to changes in input data distribution, which limits their application to problem settings with stationary input datasets. In a non-stationary environment, updating a DNN model requires parameter re-training or model fine-tuning. We propose an unsupervised source-free method to update DNN classification models. The contributions of this work are two-fold. First, we use trainable Gaussian prototypes to generate representative samples for future iterations; second, using unsupervised domain adaptation, we incrementally adapt the existing model using unlabelled data. Unlike existing methods, our approach can update a DNN model incrementally for non-stationary source and target tasks without storing past training data. We evaluated our work on incremental sentiment prediction and incremental disease prediction applications and compared our approach to state-of-the-art continual learning, domain adaptation, and ensemble learning methods. Our results show that our approach achieved improved performance compared to existing incremental learning methods. We observe minimal forgetting of past knowledge over many iterations, which can help us develop unsupervised self-learning systems.