Incremental Learning in Synthetic Aperture Radar Images Using Openmax Algorithm

Oveis, Amir Hosein; Giusti, Elisa; Ghio, Selenia; Meucci, Giulio

doi:10.1109/radarconf2351548.2023.10149627

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…In our previous studies [54]- [57], we have investigated the application of Openmax in various open set scenarios using SAR images and the potential challenges it may encounter.…”

Section: B Openmax For Open Set Recognitionmentioning

confidence: 99%

LIME-Assisted Automatic Target Recognition With SAR Images: Toward Incremental Learning and Explainability

Oveis,

Giusti,

Ghio

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Integrating an automatic target recognition (ATR) system into real-world applications presents a challenge as it may frequently encounter new samples from unseen classes. To overcome this challenge, it is necessary to adopt incremental learning, which enables the continuous acquisition of new knowledge while retaining previous knowledge. This paper introduces a novel, multi-purpose interpretability metric for ATR systems that employ synthetic aperture radar (SAR) images. The metric leverages the Local Interpretable Model-Agnostic Explanation (LIME) algorithm, enhancing human decision-making by providing a secondary measure alongside the conventional classification score. Additionally, the proposed metric is employed to analyze the robustness of Convolutional Neural Networks (CNNs) by examining the impact of target features and irrelevant background correlations on recognition results. Finally, we demonstrate the effectiveness of the proposed metric in the context of incremental learning. By utilizing the proposed interpretability metric, we select exemplars in an incremental learning scenario, resulting in improved performance and showcasing the application potential of our proposed methodology. The network is fine-tuned sequentially with unknown samples recognized by the Openmax classifier and exemplars from the old known classes, which are selected based on the proposed interpretability metric. The effectiveness of this approach is demonstrated using the publicly available MSTAR dataset.

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“…In our previous studies [54]- [57], we have investigated the application of Openmax in various open set scenarios using SAR images and the potential challenges it may encounter.…”

Section: B Openmax For Open Set Recognitionmentioning

confidence: 99%

LIME-Assisted Automatic Target Recognition With SAR Images: Toward Incremental Learning and Explainability

Oveis,

Giusti,

Ghio

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

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Few Shot Class Incremental Learning via Grassmann Manifold and Information Entropy

Gu,

Lu,

Han

et al. 2023

Electronics

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Few-shot class incremental learning is a challenging problem in the field of machine learning. It necessitates models to gradually learn new knowledge from a few samples while retaining the knowledge of old classes. Nevertheless, the limited data available for new classes not only leads to significant overfitting problems but also exacerbates the issue of catastrophic forgetting in the incremental learning process. To address the above two issues, we propose a novel framework named Grassmann Manifold and Information Entropy for Few-Shot Class Incremental Learning(GMIE-FSCIL). Different from existing methods that model parameters on the Euclidean space, our method optimizes the incremental learning network on the Grassmann manifold. More specifically, we incorporate the acquired knowledge of each class on the Grassmann manifold, ensuring the preservation of their inherent geometric properties by Grassmann Metric Learning(GML) module. Acknowledging the interconnected relationships of knowledge, with information entropy we create a neighborhood graph on Grassmann manifold to maintain inter-class structural information by Graph Information Preserving(GIP) module, thus mitigating catastrophic forgetting of learned knowledge. In our evaluation of CIFAR100, miniImageNet, and CUB200 datasets, we achieved significant improvements in terms of Avg compared to mainstream methods, with at least 2.72%, 1.21%, and 1.27% increases.

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Incremental Learning in Synthetic Aperture Radar Images Using Openmax Algorithm

Cited by 2 publications

References 29 publications

LIME-Assisted Automatic Target Recognition With SAR Images: Toward Incremental Learning and Explainability

LIME-Assisted Automatic Target Recognition With SAR Images: Toward Incremental Learning and Explainability

Few Shot Class Incremental Learning via Grassmann Manifold and Information Entropy

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