Machine unlearning (MUL) is an arising field in machine learning that seeks
to erase the learned information of specific training data points from a
trained model. Despite the recent active research in MUL within computer
vision, the majority of work has focused on ResNet-based models. Given that
Vision Transformers (ViT) have become the predominant model architecture, a
detailed study of MUL specifically tailored to ViT is essential. In this paper,
we present comprehensive experiments on ViTs using recent MUL algorithms and
datasets. We anticipate that our experiments, ablation studies, and findings
could provide valuable insights and inspire further research in this field.

机器反学习（MUL）是机器学习中的一个新兴领域，旨在从训练模型中删除特定训练数据点的学习信息。尽管在计算机视觉领域对 MUL 的研究最近非常活跃，但大部分的工作都集中在基于 ResNet 的模型上。鉴于 Vision Transformers（ViT）已成为主导的模型架构，针对 ViT 进行详细研究 MUL 的工作是必要的。本文在最近的 MUL 算法和数据集上对 ViTs 进行了全面的实验。我们期望我们的实验、消融研究和发现能够为此领域提供宝贵的见解，并激发进一步的研究。

ViT-MUL：近期应用于视觉 Transformer 的机器遗忘方法的基线研究

ViT-MUL: A Baseline Study on Recent Machine Unlearning Methods Applied  to Vision Transformers

There are two types of approaches to solving cross-lingual transfer:
multilingual pre-training implicitly aligns the hidden representations of
different languages, while the translate-test explicitly translates different
languages to an intermediate language, such as English. Translate-test has
better interpretability compared to multilingual pre-training. However, the
translate-test has lower performance than multilingual pre-training(Conneau and
Lample, 2019; Conneau et al, 2020) and can't solve word-level tasks because
translation rearranges the word order. Therefore, we propose a new
Machine-created Universal Language (MUL) as a new intermediate language. MUL
consists of a set of discrete symbols as universal vocabulary and NL-MUL
translator for translating from multiple natural languages to MUL. MUL unifies
common concepts from different languages into the same universal word for
better cross-language transfer. And MUL preserves the language-specific words
as well as word order, so the model can be easily applied to word-level tasks.
Our experiments show that translating into MUL achieves better performance
compared to multilingual pre-training, and our analyses show that MUL has good
interpretability.

本文提出了一种新的中间语言，机器创建的通用语言（MUL），将多种自然语言翻译成同一通用词，从而实现更好的跨语言转移和更好的解释性能，实验结果表明，与多语言预训练相比，将其翻译成 MUL 可获得更好的性能，并且 MUL 具有很好的解释性能。