# Autonomous Cross Domain Adaptation under Extreme Label Scarcity

arXiv: https://arxiv.org/abs/2209.01548 · HTML: https://mlsanity.com/paper/2209.01548/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 4 Sep 2022.

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

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Understanding Self-Training for Gradual Domain Adaptation](https://mlsanity.com/paper/2002.11361/) (2020)
- [Open Set Domain Adaptation: Theoretical Bound and Algorithm](https://mlsanity.com/paper/1907.08375/) (2019)
- [Unsupervised Progressive Learning and the STAM Architecture](https://mlsanity.com/paper/1904.02021/) (2019)
- [Unsupervised Continual Learning in Streaming Environments](https://mlsanity.com/paper/2109.09282/) (2021)
- [Heterogeneous domain adaptation: An unsupervised approach](https://mlsanity.com/paper/1701.02511/) (2017)
- [How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?](https://mlsanity.com/paper/2101.01104/) (2020)
- [TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation](https://mlsanity.com/paper/2106.06326/) (2021)
- [Automatic Construction of Multi-layer Perceptron Network from Streaming Examples](https://mlsanity.com/paper/1910.03437/) (2019)
- [Multi-Source Transfer Learning for Non-Stationary Environments](https://mlsanity.com/paper/1901.02052/) (2019)
- [ATL: Autonomous Knowledge Transfer from Many Streaming Processes](https://mlsanity.com/paper/1910.03434/) (2019)
- [Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation](https://mlsanity.com/paper/2007.14612/) (2020)
- [Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation](https://mlsanity.com/paper/2006.13022/) (2020)
- [Automatic Online Multi-Source Domain Adaptation](https://mlsanity.com/paper/2109.01996/) (2021)
- [An Incremental Construction of Deep Neuro Fuzzy System for Continual Learning of Non-stationary Data Streams](https://mlsanity.com/paper/1808.08517/) (2018)
- [ACDC: Online Unsupervised Cross-Domain Adaptation](https://mlsanity.com/paper/2110.01326/) (2021)

## Built on by (most cited first, 3 of 3)

- [Towards Cross-Domain Continual Learning](https://mlsanity.com/paper/2402.12490/) (2024)
- [Cross-Domain Continual Learning via CLAMP](https://mlsanity.com/paper/2405.07142/) (2024)
- [Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment](https://mlsanity.com/paper/2502.16469/) (2025)

## Abstract (arXiv)

A cross domain multistream classification is a challenging problem calling for fast domain adaptations to handle different but related streams in never-ending and rapidly changing environments. Notwithstanding that existing multistream classifiers assume no labelled samples in the target stream, they still incur expensive labelling cost since they require fully labelled samples of the source stream. This paper aims to attack the problem of extreme label shortage in the cross domain multistream classification problems where only very few labelled samples of the source stream are provided before process runs. Our solution, namely Learning Streaming Process from Partial Ground Truth (LEOPARD), is built upon a flexible deep clustering network where its hidden nodes, layers and clusters are added and removed dynamically in respect to varying data distributions. A deep clustering strategy is underpinned by a simultaneous feature learning and clustering technique leading to clustering-friendly latent spaces. A domain adaptation strategy relies on the adversarial domain adaptation technique where a feature extractor is trained to fool a domain classifier classifying source and target streams. Our numerical study demonstrates the efficacy of LEOPARD where it delivers improved performances compared to prominent algorithms in 15 of 24 cases. Source codes of LEOPARD are shared in urlhttps://github.com/wengweng001/LEOPARD.git to enable further study.