The low-frequency noise strategy, while effective in corrupting information in high-dimensional feature spaces, may have potential limitations that need to be addressed. One limitation could be the sensitivity of the noise strategy to the choice of hyperparameters, such as the mixing ratio between high-frequency and low-frequency noise. Improper tuning of these hyperparameters could lead to either insufficient corruption of information or excessive distortion, impacting the training process. To address this limitation, a more adaptive or learnable approach to determining the noise parameters could be explored. Additionally, the low-frequency noise strategy may struggle with capturing subtle variations or details in the feature volumes, especially in regions with complex structures or textures. To improve the strategy, incorporating spatially varying noise levels or adaptive noise modulation based on the local features of the volume could enhance its effectiveness in handling high-dimensional feature spaces.