Vector quantization as a post-training regularizer for passive underwater acoustic target classification

Jaeyeong Hwang; Sangmin Lee; Yoonchang Han; Donmoon Lee; Do Kyung Shin; Seung Hwan Kim; Min Young Kim; Young Dae Kim

doi:10.7776/ASK.2026.45.4.333

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2026 Vol.45, Issue 4 Next Page

Vector quantization as a post-training regularizer for passive underwater acoustic target classification 수동 수중 음향 표적 분류를 위한 사후 학습 정규화기의 벡터 양자화

31 July 2026. pp. 333-342

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Abstract

This study proposes a Vector Quantization (VQ) based regularizer to mitigate the overfitting of overparameterized deep learning models in underwater acoustic target recognition, where training data are scarce. We introduce a discrete bottleneck layer that is applied after a classification network has been fully trained on the target task. The bottleneck compresses the continuous representation into a finite codebook and reconstructs it before classification. To ensure stable optimization, we incorporate an autoencoder initialization and a model selection rule based on validation loss and codebook usage. Using the ShipsEar dataset, the proposed method achieves 79.7 % accuracy on Residual Networks (ResNet)-50, outperforming the baseline by 4.8 % with lower training variance. Through ablation studies, we demonstrate that the discretization process, post-training setup, autoencoder initialization, and the proposed model selection rule are jointly necessary to achieve the regularization gains, showing its effectiveness as a representation level constraint for underwater acoustic classification under data scarcity.

Keywords

Passive sonar

Neural networks

Regularization

Vector Quantization (VQ)

Information Bottleneck (IB)

Vessel classification

본 연구에서는 데이터가 부족한 수중 음향 표적 인식 환경에서 신경망의 과적합을 완화하기 위해 벡터 양자화 기반의 정규화를 제안하였다. 이는 분류 네트워크가 목표 작업에 대해 학습된 후에 적용되는 이산 병목 층을 도입하는 것으로, 연속적인 임베딩을 유한한 코드북으로 압축한 후, 분류 단계 이전에 이를 재구성하는 방식으로 구현되었다. 추가로, 안정적인 최적화를 보장하기 위해 오토인코더 초기화와 코드북 사용량에 기반한 모델 선택 규칙을 제안하였다. ShipsEar 데이터셋에서 진행된 실험 결과, 제안 방식은 기존 Residual Networks(ResNet)-50 분류기 대비 4.8 % 향상된 79.7 %의 정확도를 달성하며 정규화 이득 측면의 효과성을 입증하였다. 또한, 이산 병목, 사후 학습, 오토인코더 초기화, 그리고 제안된 모델 선택 규칙은 효과적인 정규화를 달성하기 위해 모두 필요함을 입증하였다.

키워드

수동 소나

신경망

정규화

벡터 양자화(Vector Quantization, VQ)

정보 병목(Information Bottleneck, IB)

선박 분류

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Information

Publisher :The Acoustical Society of Korea
Publisher(Ko) :한국음향학회
Journal Title :The Journal of the Acoustical Society of Korea
Journal Title(Ko) :한국음향학회지
Volume : 45
No :4
Pages :333-342
Received Date : 2026-06-19
Accepted Date : 2026-07-16
DOI :https://doi.org/10.7776/ASK.2026.45.4.333

The Journal of the Acoustical Society of KoreaISSN:1225-4428(Print) 2287-3775(Online)한국음향학회

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