The largest neural networks are organized on the cell-scale corpora in modern-day machine study. In this paradigm, the distribution of large, with heterogeneous is not usually synchronized with the app's domain. This activity looks to change the distribution of your not when a person has a small data sample indicating targets for intended assessment. We propose algorithm motivated by the latest construction of this setting as an online, biilevel problem. With graciousness in mind, our algorithm puts preying gradients in the training areas that are likely to improve the loss of intended distribution. In fact, we show that in some ways, this method is beneficial for existing strategies in publications that are in adapting and unsuccessful. We suggest a simple test for testing where our approach can be expected to work properly and point to additional research to address current limits.
