The proposed asymmetric distillation framework can be extended to other types of data augmentation beyond mixed-sample approaches by incorporating different augmentation techniques that introduce variations in the input data. For example, techniques like CutOut, MixUp, or Rand. Quantization can be integrated into the framework to provide diverse training samples for the model. By feeding the teacher model with augmented data generated using these techniques, the student model can learn to focus on class-specific features while discarding common or non-discriminative features. This approach can help improve the model's performance in both closed-set and open-set recognition tasks across a wider range of data augmentation strategies.