Figure 2.

Training Dynamics for Learned EM Sensing Applied to an Imaging Task

The dependence of the training and test loss functions on the progress of iterative epochs is shown for different numbers of coding patterns M, i.e., M = 3, 9, 15, and 20. The continuous lines indicate the training loss and the dashed lines indicate the test loss. The control coding patterns of the metasurface are initialized randomly (top) or PCA-based (bottom). During stage I, only the digital decoder weights $\mathbf{\Phi }$ are optimized. Then, during stage II, both the physical weights $\mathcal{C}$ and the digital weights $\mathbf{\Phi }$ are jointly optimized. The presented results show a remarkable improvement of the image quality achieved by using the joint optimization of $\mathcal{C}$ and $\mathbf{\Phi }$ during stage II, compared with that based on solely optimizing $\mathbf{\Phi }$ (i.e., the end of stage I). The effect is especially striking when the number of measurements is very limited.