# Domain Adaptation with Adversarial Training and Graph Embeddings

arXiv: https://arxiv.org/abs/1805.05151 · HTML: https://mlsanity.com/paper/1805.05151/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2018.

## Built on by (most cited first, 30 of 30)

- [Domain Adaptation for Time Series Forecasting via Attention Sharing](https://mlsanity.com/paper/2102.06828/) (2021)
- [Evaluation Gaps in Machine Learning Practice](https://mlsanity.com/paper/2205.05256/) (2022)
- [UDALM: Unsupervised Domain Adaptation through Language Modeling](https://mlsanity.com/paper/2104.07078/) (2021)
- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [Graph-Relational Domain Adaptation](https://mlsanity.com/paper/2202.03628/) (2022)
- [Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation](https://mlsanity.com/paper/2205.11678/) (2022)
- [Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning](https://mlsanity.com/paper/2402.16374/) (2024)
- [Towards Data-centric Graph Machine Learning: Review and Outlook](https://mlsanity.com/paper/2309.10979/) (2023)
- [Semi-Supervised Text Classification via Self-Pretraining](https://mlsanity.com/paper/2109.15300/) (2021)
- [Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks](https://mlsanity.com/paper/2111.01340/) (2021)
- [Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification](https://mlsanity.com/paper/2203.02912/) (2022)
- [Explainable Misinformation Detection Across Multiple Social Media Platforms](https://mlsanity.com/paper/2203.11724/) (2022)
- [Cross-Lingual and Cross-Domain Crisis Classification for Low-Resource Scenarios](https://mlsanity.com/paper/2209.02139/) (2022)
- [Enhancing Crisis-Related Tweet Classification with Entity-Masked Language Modeling and Multi-Task Learning](https://mlsanity.com/paper/2211.11468/) (2022)
- [Near-real-time Earthquake-induced Fatality Estimation using Crowdsourced Data and Large-Language Models](https://mlsanity.com/paper/2312.03755/) (2023)
- [Multi-task transfer learning for finding actionable information from crisis-related messages on social media](https://mlsanity.com/paper/2102.13395/) (2021)
- [Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management](https://mlsanity.com/paper/2104.01436/) (2021)
- [SuMe: A Dataset Towards Summarizing Biomedical Mechanisms](https://mlsanity.com/paper/2205.04652/) (2022)
- [Identification of Fine-Grained Location Mentions in Crisis Tweets](https://mlsanity.com/paper/2111.06334/) (2021)
- [A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2201.12549/) (2022)
- [CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification](https://mlsanity.com/paper/2310.14627/) (2023)
- [Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance](https://mlsanity.com/paper/2312.14943/) (2023)
- [Crisis Domain Adaptation Using Sequence-to-sequence Transformers](https://mlsanity.com/paper/2110.08015/) (2021)
- [How does a Pre-Trained Transformer Integrate Contextual Keywords? Application to Humanitarian Computing](https://mlsanity.com/paper/2111.04052/) (2021)
- [Supervised Graph Contrastive Pretraining for Text Classification](https://mlsanity.com/paper/2112.11389/) (2021)
- [Domain Adaptation with Category Attention Network for Deep Sentiment Analysis](https://mlsanity.com/paper/2112.15290/) (2021)
- [Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster](https://mlsanity.com/paper/2302.13403/) (2023)
- [Generalizing to Unseen Disaster Events: A Causal View](https://mlsanity.com/paper/2511.10120/) (2025)
- [Rethinking Graph Generalization through the Lens of Sharpness-Aware Minimization](https://mlsanity.com/paper/2602.08855/) (2026)
- [LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification](https://mlsanity.com/paper/2605.08448/) (2026)

## Abstract (arXiv)

The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. However, obtaining labeled data is a big challenge in many real-world problems. In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data distributions between the source and the target domains. In this paper, we study the problem of classifying social media posts during a crisis event (e.g., Earthquake). For that, we use labeled and unlabeled data from past similar events (e.g., Flood) and unlabeled data for the current event. We propose a novel model that performs adversarial learning based domain adaptation to deal with distribution drifts and graph based semi-supervised learning to leverage unlabeled data within a single unified deep learning framework. Our experiments with two real-world crisis datasets collected from Twitter demonstrate significant improvements over several baselines.