# Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation

arXiv: https://arxiv.org/abs/2308.16633 · HTML: https://mlsanity.com/paper/2308.16633/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning](https://mlsanity.com/paper/2003.04390/) (2020)
- [Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey](https://mlsanity.com/paper/2007.02106/) (2020)
- [Shot in the Dark: Few-Shot Learning with No Base-Class Labels](https://mlsanity.com/paper/2010.02430/) (2020)
- [When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation](https://mlsanity.com/paper/2308.07093/) (2023)
- [Few-shot Learning via Dependency Maximization and Instance Discriminant Analysis](https://mlsanity.com/paper/2109.02820/) (2021)

## Abstract (arXiv)

Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends heavily on a large amount of training data. The insufficiency of labeled training SAR images limits the recognition performance and even invalidates some ATR methods. Furthermore, under few labeled training data, many existing CNNs are even ineffective. To address these challenges, we propose a Semi-supervised SAR ATR Framework with transductive Auxiliary Segmentation (SFAS). The proposed framework focuses on exploiting the transductive generalization on available unlabeled samples with an auxiliary loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR samples and information residue loss (IRL) in training, the framework can employ the proposed training loop process and gradually exploit the information compilation of recognition and segmentation to construct a helpful inductive bias and achieve high performance. Experiments conducted on the MSTAR dataset have shown the effectiveness of our proposed SFAS for few-shot learning. The recognition performance of 94.18% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results. Facing variances of EOCs, the recognition ratios are higher than 88.00% when 10 training samples each class.