Removing bias for out-of-distribution generalization - Qi, Jiaxin (Nanyang Technological University, 2023)
Deep models have a strong ability to fit the training data, and thus can achieve high performance when the testing data is sampled from the same distribution as the training. However, in practice, the deep models fail to perform perfectly because the testing data is usually Out-of-Distribution (OOD) compared to the training, which is known as the OOD Generalization problem. The underlying reason is that, in the training, besides the causal effect, i.e., the causalities between inputs and outputs which describe the data generation process and will not change under any data distribution, the models also learn the bias, i.e., the spurious correlations between inputs and outputs which only exists in the training distribution, and thus learning such bias will make the model fail to generalize to OOD data.