Richard discusses the surprising relationship between deep learning architectures and human perception, revealing that deeper networks may not correlate better with how humans perceive images. While models like Alexnet achieve peak correlation due to their seven-layer design, deeper networks, despite their superior performance on tasks, may diverge from human-like perception. Insights from neuroscience support this finding, suggesting a complex interplay between model depth and perceptual accuracy.
