# Maximum Batch Frobenius Norm for Multi-Domain Text Classification

arXiv: https://arxiv.org/abs/2202.00537 · HTML: https://mlsanity.com/paper/2202.00537/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 29 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [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)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Adversarial Multi-task Learning for Text Classification](https://mlsanity.com/paper/1704.05742/) (2017)
- [Multinomial Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/1802.05694/) (2018)
- [Conditional Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2102.10176/) (2021)
- [Dual Adversarial Co-Learning for Multi-Domain Text Classification](https://mlsanity.com/paper/1909.08203/) (2019)
- [Mixup Regularized Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2102.00467/) (2021)
- [Towards Category and Domain Alignment: Category-Invariant Feature Enhancement for Adversarial Domain Adaptation](https://mlsanity.com/paper/2108.06583/) (2021)

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

- [GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise](https://mlsanity.com/paper/2411.05939/) (2024)
- [Margin Discrepancy-based Adversarial Training for Multi-Domain Text Classification](https://mlsanity.com/paper/2403.00888/) (2024)
- [Multi-Layer Attention is the Amplifier of Demonstration Effectiveness](https://mlsanity.com/paper/2508.00385/) (2025)

## Abstract (arXiv)

Multi-domain text classification (MDTC) has obtained remarkable achievements due to the advent of deep learning. Recently, many endeavors are devoted to applying adversarial learning to extract domain-invariant features to yield state-of-the-art results. However, these methods still face one challenge: transforming original features to be domain-invariant distorts the distributions of the original features, degrading the discriminability of the learned features. To address this issue, we first investigate the structure of the batch classification output matrix and theoretically justify that the discriminability of the learned features has a positive correlation with the Frobenius norm of the batch output matrix. Based on this finding, we propose a maximum batch Frobenius norm (MBF) method to boost the feature discriminability for MDTC. Experiments on two MDTC benchmarks show that our MBF approach can effectively advance the performance of the state-of-the-art.