The many successes of deep neural networks (DNNs) over the past decade have
largely been driven by computational scale rather than insights from biological
intelligence. Here, we explore if these trends have also carried concomitant
improvements in explaining the visual strategies humans rely on for object
recognition. We do this by comparing two related but distinct properties of
visual strategies in humans and DNNs: where they believe important visual
features are in images and how they use those features to categorize objects.
Across 84 different DNNs trained on ImageNet and three independent datasets
measuring the where and the how of human visual strategies for object
recognition on those images, we find a systematic trade-off between DNN
categorization accuracy and alignment with human visual strategies for object
recognition. State-of-the-art DNNs are progressively becoming less aligned with
humans as their accuracy improves. We rectify this growing issue with our
neural harmonizer: a general-purpose training routine that both aligns DNN and
human visual strategies and improves categorization accuracy. Our work
represents the first demonstration that the scaling laws that are guiding the
design of DNNs today have also produced worse models of human vision. We
release our code and data at this https URL to help
the field build more human-like DNNs.

研究发现，尽管深度神经网络（DNNs）的成功主要是由于计算规模，而不是基于生物智能的洞见，但是随着精度的提高，DNNs 与人类视觉策略的一致性逐渐降低，但是神经谐振器的出现可以使 DNNs 与人类的视觉策略相一致并提高分类精度。