Figure 6.

Results of Experiment 3. The model was trained on naturalistic stimuli, comparing three reconstruction tasks. (a) Original image. (b) Pixel intensity-based reconstruction task with MSE loss (see Equations 1–3). (c) Perceptual reconstruction task, using VGG feature loss (see Equation 4; d is set equal to 3). (d) Semantic boundary reconstruction task, using weighted BCE loss (see Equation 5) between the reconstruction and the ground truth semantic boundary label (i.e. a binary, boundary-based, version of the ground truth label from the dataset). (e) Simulated prosthetic percept after conventional image preprocessing with (left) Canny edge detection (Canny, 1986) and (right) holistically nested edge detection (Xie & Tu, 2017).