论文标题
学习更多可能不是更好的：在视觉和语言任务中的知识转移性
Learning More May Not Be Better: Knowledge Transferability in Vision and Language Tasks
论文作者
论文摘要
训练视觉和语言模型的更多数据总是更好吗？我们研究多模式任务中的知识可传递性。当前机器学习的趋势是假设通过从不同任务加入多个数据集将有所改善。但是，我们表明，并非所有知识都可以很好地转移或对相关任务产生积极影响，即使它们共享共同的目标。我们基于数百种分为4组的视觉和语言任务进行了数百个跨表现的分析。同一组中的任务容易相互改进，但结果表明并非总是如此。其他因素（例如数据集大小或训练阶段）也对知识的转移程度也有很大的影响。
Is more data always better to train vision-and-language models? We study knowledge transferability in multi-modal tasks. The current tendency in machine learning is to assume that by joining multiple datasets from different tasks their overall performance will improve. However, we show that not all the knowledge transfers well or has a positive impact on related tasks, even when they share a common goal. We conduct an exhaustive analysis based on hundreds of cross-experiments on 12 vision-and-language tasks categorized in 4 groups. Whereas tasks in the same group are prone to improve each other, results show that this is not always the case. Other factors such as dataset size or pre-training stage have also a great impact on how well the knowledge is transferred.
