Spectral-Spatial Interpretation (SSI) 发布：通过空间单元和频谱通道的符号归因解释模型分数
Spectral-Spatial Interpretation
Spectral-Spatial Interpretation (SSI) 发布，使用符号归因解释模型分数，为每个空间单元和频谱通道分配贡献度。SSI 在三个数据集和两种掩码基线下的三个指标平均表现最佳，例如 HLS 联合删除的均值基线得分 0.1545 高于 KernelSHAP 的 0.2096。SSI 在空间定位、频谱 Hit@K 和通道删除方面领先于比较方法。
🔥🔥🔥Which spectral channels matter, and where?
SSI explains a model score with signed attributions for each spatial cell and channel. It keeps the surrounding image fixed while allocating a cell's contribution among its channels using Shapley values.
A channel's contribution can change across an image. SSI asks how the channels within one spatial cell affect the chosen model score, conditioned on the observed values of all other cells.
✅ SSI has the best reported mean for all three metrics on all three datasets under both masking baselines. For example, mean-baseline HLS joint deletion is .1545 for SSI versus .2096 for KernelSHAP, the strongest non-SSI method on that metric.
✅ SSI leads the compared methods in spatial localization, spectral Hit@K, and channel deletion across the evaluated datasets.
来源: Hugging Face Daily Papers · huggingface.co