PREDICTING THE GENERALIZATION GAP IN DEEP MODELS USING ANCHORING

We address the problem of predicting the generalization gap of deep neural networks under large, natural, and synthetic distribution shifts between source and target domains. This is crucial in understanding how models behave in uncontrollable `in-the-wild' scenarios, but existing techniques fail when target domain becomes very different from the source. Accurately capturing the relationship and distance between the source and target domains is critical for a reliable post-hoc estimation of generalization. In this paper, we propose a novel strategy for directly predicting accuracy on unseen target data with the help of anchoring and pre-text encoding in predictive models. Anchoring has been shown previously to perform effectively in characterizing domain shifts, which we exploit for predicting the generalization gap. Our experiments on the PACS dataset along with synthetic ablations indicate that our approach produces well calibrated accuracy estimates outperforming existing baselines.