In practical scenarios, it is often the case that the available training data within the target domain only exist for a limited number of classes, with the remaining classes only available within surrogate domains. We show that including the target domain in training when there exist disjoint classes between the target and surrogate domains creates significant negative transfer, and causes performance to significantly decrease compared to training without the target domain at all. We hypothesize that this negative transfer is due to an intermediate shortcut that only occurs when multiple source domains are present, and provide experimental evidence that this may be the case. We show that this phenomena occurs on over 25 distinct domain shifts, both synthetic and real, and in many cases deteriorates the performance to well worse than random, even when using state-of-the-art domain adaptation methods.

翻译：在实际场景中，目标域内的可用训练数据通常仅包含有限数量的类别，其余类别仅存在于替代域中。我们表明，当目标域与替代域之间存在不相交类别时，在训练中包含目标域会引发显著的负迁移，导致性能相较于完全不使用目标域训练时大幅下降。我们假设这种负迁移是由于多源域存在时才会出现的中间捷径所致，并提供实验证据支持这一假设。我们证明，该现象在超过25种不同的域偏移（包括合成与真实场景）中均会出现，且在许多情况下，即使采用最先进的域适应方法，性能也会恶化至远低于随机水平。