Questions

1. Apply Mixup interpolation to the Kaggle spam detection NLP dataset used in the chapter. See if Mixup helps to improve the model performance. You can refer to the paper Augmenting Data with Mixup for Sentence Classification: An Empirical Study by Guo et al. (https://arxiv.org/pdf/1905.08941.pdf) for further reading.
2. Refer to the FMix paper [21] and implement the FMix augmentation technique. Apply it to the Caltech101 dataset. See whether model performance improves by using FMix over the baseline model performance.
3. Apply the EOS technique described in the chapter to the CIFAR-10-LT (the long-tailed version of CIFAR-10) dataset, and see whether the model performance improves for the most imbalanced classes.
4. Apply the MDSA techniques we studied in this chapter to the CIFAR-10-LT dataset, and see whether the model performance improves for the most imbalanced classes.