In our previous work, we proposed an unsupervised deep learning method for parallel MR cardiac imaging via time interleaved sampling. The comparisons with classical methods on in vivo data have shown that this method can achieve improved reconstruction results. However, the proposed unsupervised framework is based on the time interleaved sampling scheme. Does the model trained with time interleaved undersampling pattern have good generalization to other sampling patterns? In this paper, we will explore the generalization performance of the learned unsupervised deep learning method under different sampling patterns.