Appendix of Nets Expanding Receptive Field for Dense Prediction A Supplementary Materials for Experiments

In the simulations in subsection 3.2, all linear networks have The backbone architecture consists of a stack of 12 Conv-LSTM modules, and each module contains 32 units (channels). The backbone architecture is illustrated in Figure 7. To demonstrate ARMA networks' applicability to image segmentation, we evaluate it on a challenging The network architecture is illustrated in Figure 15a. The experimental results are summarized in Table 5. Since image classifications tasks do not require convolu-tional layers to have large receptive fields, the learned autoregressive coefficients concentrate around 0, as shown in Figure 6.