论文标题
关于转移学习中目标数据的价值
On the Value of Target Data in Transfer Learning
论文作者
论文摘要
我们旨在了解任何给定数量的源数据中其他标记或未标记的目标数据在转移学习中的价值；这是由最小化采样成本最小化的实用问题所激发的，因此，目标数据通常比源数据更难或更昂贵，但可以产生更好的准确性。为此，我们根据源和目标样本量建立了第一个最小值率，并表明性能限制是由源和目标之间的新差异概念捕获的，我们称为传输指数。
We aim to understand the value of additional labeled or unlabeled target data in transfer learning, for any given amount of source data; this is motivated by practical questions around minimizing sampling costs, whereby, target data is usually harder or costlier to acquire than source data, but can yield better accuracy. To this aim, we establish the first minimax-rates in terms of both source and target sample sizes, and show that performance limits are captured by new notions of discrepancy between source and target, which we refer to as transfer exponents.
