mlsanityData to 14 Oct 2026

BSS-Bench: Towards Reproducible and Effective Band Selection Search

arXiv 2312.14570 ↗First posted 22 Dec 2023cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

The key technology to overcome the drawbacks of hyperspectral imaging (expensive, high capture delay, and low spatial resolution) and make it widely applicable is to select only a few representative bands from hundreds of bands. However, current band selection (BS) methods face challenges in fair comparisons due to inconsistent train/validation settings, including the number of bands, dataset splits, and retraining settings. To make BS methods easy and reproducible, this paper presents the first band selection search benchmark (BSS-Bench) containing 52k training and evaluation records of numerous band combinations (BC) with different backbones for various hyperspectral analysis tasks. The creation of BSS-Bench required a significant computational effort of 1.26k GPU days. By querying BSS-Bench, BS experiments can be performed easily and reproducibly, and the gap between the searched result and the best achievable performance can be measured. Based on BSS-Bench, we further discuss the impact of various factors on BS, such as the number of bands, unsupervised statistics, and different backbones. In addition to BSS-Bench, we present an effective one-shot BS method called Single Combination One Shot (SCOS), which learns the priority of any BCs through one-time training, eliminating the need for repetitive retraining on different BCs. Furthermore, the search process of SCOS is flexible and does not require training, making it efficient and effective. Our extensive evaluations demonstrate that SCOS outperforms current BS methods on multiple tasks, even with much fewer bands. Our BSS-Bench and codes are available in the supplementary material and will be publicly available.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 58 references that are in the map. First posted on arXiv 22 Dec 2023.

Builds on

26 of 58 references.

References · 26

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  3. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,071
  4. 2020NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Searchcited by 416
  5. 2019NAS-Bench-101: Towards Reproducible Neural Architecture Searchcited by 344
  6. 2021HINet: Half Instance Normalization Network for Image Restorationcited by 237
  7. 2019Neural Predictor for Neural Architecture Searchcited by 118
  8. 2018Rank Minimization for Snapshot Compressive Imagingcited by 99
  9. 2019HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classificationcited by 92
  10. 2020Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imagingcited by 78
  11. 2022MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstructioncited by 72
  12. 2020Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarkscited by 67
14 more
  1. 2022HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imagingcited by 66
  2. 2022Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imagingcited by 66
  3. 2021Deep Gaussian Scale Mixture Prior for Spectral Compressive Imagingcited by 58
  4. 2021Plug-and-Play Algorithms for Video Snapshot Compressive Imagingcited by 42
  5. 2021Self-supervised Neural Networks for Spectral Snapshot Compressive Imagingcited by 35
  6. 2021HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imagingcited by 26
  7. 2022Shapley-NAS: Discovering Operation Contribution for Neural Architecture Searchcited by 23
  8. 2021Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classificationcited by 12
  9. 2022AIO-P: Expanding Neural Performance Predictors Beyond Image Classificationcited by 8
  10. 2023β-DARTS++: Bi-level Regularization for Proxy-robust Differentiable Architecture Searchcited by 7
  11. 2021Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networkscited by 4
  12. 2023A Simple Adaptive Unfolding Network for Hyperspectral Image Reconstructioncited by 2
  13. 2022Band selection and classification of hyperspectral images by minimizing normalized mutual informationcited by 1
  14. 2023Hyperspectral Image Compression Using Implicit Neural Representationcited by 1

Built on by

Not cited yet.