ReAlnet: Achieving More Human Brain-Like Vision via Human Neural
  Representational Alignment
Zitong Lu Yile Wang Julie D. Golomb
Despite the remarkable strides made in artificial intelligence, current
object recognition models still lag behind in emulating the mechanism of visual
information processing in human brains. Recent studies have highlighted the
potential of using neural data to mimic brain processing; however, these often
reply on invasive neural recordings from non-human subjects, leaving a critical
gap in our understanding of human visual perception and the development of more
human brain-like vision models. Addressing this gap, we present, for the first
time, "Re(presentational)Al(ignment)net", a vision model aligned with human
brain activity based on non-invasive EEG recordings, demonstrating a
significantly higher similarity to human brain representations. Our innovative
image-to-brain multi-layer encoding alignment framework not only optimizes
multiple layers of the model, marking a substantial leap in neural alignment,
but also enables the model to efficiently learn and mimic human brain's visual
representational patterns across object categories and different neural data
modalities. Furthermore, we discover that alignment with human brain
representations improves the model's adversarial robustness. Our findings
suggest that ReAlnet sets a new precedent in the field, bridging the gap
between artificial and human vision, and paving the way for more brain-like
artificial intelligence systems.