The article discusses the implementation of LieBN, a novel batch normalization method tailored for SPD manifold networks. Through extensive experiments on datasets like Radar, HDM05, and FPHA, it is shown that LieBN outperforms standard SPDBN techniques. The performance varies significantly with different LieBN configurations, highlighting the importance of using the optimal metrics for each dataset. Additionally, the research emphasizes the efficiency gains achieved during training, indicating the potential of LieBN as an essential advancement in SPD network architectures.
In our experiments with SPDNet on various datasets, we noted that the performance of the LieBN layer was highly dependent on the choice of metrics, demonstrating unique optimal configurations for each dataset.
The experimental results illustrate that the LieBN method significantly enhances training efficiency and effectiveness compared to traditional SPDBN, particularly when the appropriate deformed metrics are applied.
Collection
[
|
...
]