# Underwater Acoustic Target Recognition based on Smoothness-inducing Regularization and Spectrogram-based Data Augmentation

arXiv: https://arxiv.org/abs/2306.06945 · HTML: https://mlsanity.com/paper/2306.06945/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://mlsanity.com/paper/1911.03437/) (2019)
- [Eliminate Deviation with Deviation for Data Augmentation and a General Multi-modal Data Learning Method](https://mlsanity.com/paper/2101.08533/) (2021)
- [Underwater-Art: Expanding Information Perspectives With Text Templates For Underwater Acoustic Target Recognition](https://mlsanity.com/paper/2305.19612/) (2023)
- [Adaptive ship-radiated noise recognition with learnable fine-grained wavelet transform](https://mlsanity.com/paper/2306.01002/) (2023)
- [Underwater Target Recognition based on Multi-Decision LOFAR Spectrum Enhancement: A Deep Learning Approach](https://mlsanity.com/paper/2104.12362/) (2021)

## Built on by (most cited first, 5 of 5)

- [Unraveling Complex Data Diversity in Underwater Acoustic Target Recognition through Convolution-based Mixture of Experts](https://mlsanity.com/paper/2402.11919/) (2024)
- [Advancing Robust Underwater Acoustic Target Recognition through Multi-task Learning and Multi-Gate Mixture-of-Experts](https://mlsanity.com/paper/2411.02787/) (2024)
- [Adversarial multi-task underwater acoustic target recognition: towards robustness against various influential factors](https://mlsanity.com/paper/2411.02848/) (2024)
- [AquaSignal: An Integrated Framework for Robust Underwater Acoustic Analysis](https://mlsanity.com/paper/2505.14285/) (2025)
- [Multi-Representation Attention Framework for Underwater Bioacoustic Denoising and Recognition](https://mlsanity.com/paper/2510.26838/) (2025)

## Abstract (arXiv)

Underwater acoustic target recognition is a challenging task owing to the intricate underwater environments and limited data availability. Insufficient data can hinder the ability of recognition systems to support complex modeling, thus impeding their advancement. To improve the generalization capacity of recognition models, techniques such as data augmentation have been employed to simulate underwater signals and diversify data distribution. However, the complexity of underwater environments can cause the simulated signals to deviate from real scenarios, resulting in biased models that are misguided by non-true data. In this study, we propose two strategies to enhance the generalization ability of models in the case of limited data while avoiding the risk of performance degradation. First, as an alternative to traditional data augmentation, we utilize smoothness-inducing regularization, which only incorporates simulated signals in the regularization term. Additionally, we propose a specialized spectrogram-based data augmentation strategy, namely local masking and replicating (LMR), to capture inter-class relationships. Our experiments and visualization analysis demonstrate the superiority of our proposed strategies.