# ADMP: An Adversarial Double Masks Based Pruning Framework For Unsupervised Cross-Domain Compression

arXiv: https://arxiv.org/abs/2006.04127 · HTML: https://mlsanity.com/paper/2006.04127/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

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

- [Slimmable Domain Adaptation](https://mlsanity.com/paper/2206.06620/) (2022)
- [DUDA: Distilled Unsupervised Domain Adaptation for Lightweight Semantic Segmentation](https://mlsanity.com/paper/2504.09814/) (2025)
- [Toward domain generalized pruning by scoring out-of-distribution importance](https://mlsanity.com/paper/2210.13810/) (2022)

## Abstract (arXiv)

Despite the recent progress of network pruning, directly applying it to the Internet of Things (IoT) applications still faces two challenges, i.e. the distribution divergence between end and cloud data and the missing of data label on end devices. One straightforward solution is to combine the unsupervised domain adaptation (UDA) technique and pruning. For example, the model is first pruned on the cloud and then transferred from cloud to end by UDA. However, such a naive combination faces high performance degradation. Hence this work proposes an Adversarial Double Masks based Pruning (ADMP) for such cross-domain compression. In ADMP, we construct a Knowledge Distillation framework not only to produce pseudo labels but also to provide a measurement of domain divergence as the output difference between the full-size teacher and the pruned student. Unlike existing mask-based pruning works, two adversarial masks, i.e. soft and hard masks, are adopted in ADMP. So ADMP can prune the model effectively while still allowing the model to extract strong domain-invariant features and robust classification boundaries. During training, the Alternating Direction Multiplier Method is used to overcome the binary constraint of {0,1}-masks. On Office31 and ImageCLEF-DA datasets, the proposed ADMP can prune 60% channels with only 0.2% and 0.3% average accuracy loss respectively. Compared with the state of art, we can achieve about 1.63x parameters reduction and 4.1% and 5.1% accuracy improvement.