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A nuclear norm-induced robust and lightweight relation network for few-shots classification of hyperspectral images

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Abstract

Few-shots learning is a popular transfer learning paradigm and leverages an additional source of side information to compensate for the limited labelled training exemplars in application domains like healthcare, military and space. Most few-shots learners do not capture useful data manifolds and have numerous trainable parameters. This large parameter space makes the inference computationally expensive and energy-draining. Consequently, these models are not robust and cannot be deployed on devices with limited computation (energy) faculties. This work proposes NucNormFSL, a novel nuclear norm-induced lightweight relation network, for the few-shots classification of hyperspectral images. The embedding and relation modules in the proposed network are trained end-to-end by minimizing a dictionary learning-based loss function with only a few trainable parameters. Additionally, the embedding module loss function is regularized using a nuclear norm to give low-ranked solutions that are robust to environmental noise; lastly, a relative reconstruction loss metric is introduced to quantify the embedding’s robustness to noise. Experiments are conducted on four benchmark hyperspectral datasets, namely, Indian Pines, Pavia Center, Pavia University and Salinas dataset; the relative reconstruction loss values computed confirm the robustness of the embeddings and hence the proposed network to environmental noise. Additionally, the proposed network’s performance is compared with the baseline (without a nuclear norm term in its embedding loss function) model. The proposed approach beats the baseline for most few-shots settings and datasets and remains competitive with the state-of-the-art despite being severely lightweight. In this way, the proposed network is futuristic, lightweight and immune to noise; consequently, it can be deployed in noisy environments on devices with limited computation facilities.

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Data Availability

The experimental data will be made available from the corresponding author upon reasonable request

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The codes will be made publicly available upon acceptance.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha O’ Anusandhan University, Bhubaneshwar, Odisha, India

    Upendra Pratap Singh

  2. Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, Uttar Pradesh, India

    Krishna Pratap Singh

  3. School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Manoj Thakur

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Contributions

All authors have contributed to this research study in different capacities. For instance, the study was conceptualised by Upendra Pratap Singh and Krishna Pratap Singh, while Upendra Pratap Singh performed material preparation, data analysis and experiments. Upendra Pratap Singh wrote the manuscript’s first draft, and Manoj Thakur and Krishna Pratap Singh suggested relevant improvements. The final version of the manuscript was proofread and approved by all the authors.

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Correspondence to Upendra Pratap Singh.

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Singh, U.P., Singh, K.P. & Thakur, M. A nuclear norm-induced robust and lightweight relation network for few-shots classification of hyperspectral images. Multimed Tools Appl 83, 9279–9306 (2024). https://doi.org/10.1007/s11042-023-15500-z

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