Abstract
We present SOSELETO (SOurce SELEction for Target Optimization), a new methodfor exploiting a source dataset to solve a classification problem on a targetdataset. SOSELETO is based on the following simple intuition: some sourceexamples are more informative than others for the target problem. To capturethis intuition, source samples are each given weights; these weights are solvedfor jointly with the source and target classification problems via a bileveloptimization scheme. The target therefore gets to choose the source sampleswhich are most informative for its own classification task. Furthermore, thebilevel nature of the optimization acts as a kind of regularization on thetarget, mitigating overfitting. SOSELETO may be applied to both classictransfer learning, as well as the problem of training on datasets with noisylabels; we show state of the art results on both of these problems.