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Found 4 papers, 4 papers with code
Bridging Sensor Gaps via Single-Direction Tuning for Hyperspectral Image Classification
In this paper, aiming to solve this problem, we propose the single-direction tuning (SDT) strategy, which serves as a bridge, allowing us to leverage existing labeled HSI datasets even RGB datasets to enhance the performance on new HSI datasets with limited samples.
Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image Classification
Compared with search spaces proposed in previous works, the proposed hybrid search space is more aligned with the characteristic of HSI data, that is, HSIs have a relatively low spatial resolution and an extremely high spectral resolution.
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification
Correspondingly, different models need to be designed for different datasets, which further increases the workload of designing architectures; 2) the mainstream framework is a patch-to-pixel framework.
Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration
For the inner search space, we propose a layer-wise architecture sharing strategy (LWAS), resulting in more flexible architectures and better performance.
