Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection
<|reference_start|>Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral Band Selection: Band selection plays a crucial role in hyperspectral image classification by removing redundant and noisy bands and retaining discriminative ones. However, most existing deep learning-based methods are aimed at dealing with a specific band selection dataset, and need to retrain parameters for new datasets, which significantly limits their generalizability.To address this issue, a novel multi-teacher multi-objective meta-learning network (M$^3$BS) is proposed for zero-shot hyperspectral band selection. In M$^3$BS, a generalizable graph convolution network (GCN) is constructed to generate dataset-agnostic base, and extract compatible meta-knowledge from multiple band selection tasks. To enhance the ability of meta-knowledge extraction, multiple band selection teachers are introduced to provide diverse high-quality experiences.strategy Finally, subsequent classification tasks are attached and jointly optimized with multi-teacher band selection tasks through multi-objective meta-learning in an end-to-end trainable way. Multi-objective meta-learning guarantees to coordinate diverse optimization objectives automatically and adapt to various datasets simultaneously. Once the optimization is accomplished, the acquired meta-knowledge can be directly transferred to unseen datasets without any retraining or fine-tuning. Experimental results demonstrate the effectiveness and efficiency of our proposed method on par with state-of-the-art baselines for zero-shot hyperspectral band selection.<|reference_end|>
@article{feng2024multi-teacher,
    title={Multi-Teacher Multi-Objective Meta-Learning for Zero-Shot Hyperspectral
  Band Selection},
    author={Jie Feng, Xiaojian Zhong, Di Li, Weisheng Dong, Ronghua Shang, and
  Licheng Jiao},
    journal={arXiv preprint arXiv:2406.07949},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07949},
    primaryClass={cs.CV}
}