The backpropagation (BP) algorithm is often thought to be biologically
implausible in the brain. One of the main reasons is that BP requires symmetric
weight matrices in the feedforward and feedback pathways. To address this
"weight transport problem" (Grossberg, 1987), two more biologically plausible
algorithms, proposed by Liao et al. (2016) and Lillicrap et al. (2016), relax
BP's weight symmetry requirements and demonstrate comparable learning
capabilities to that of BP on small datasets. However, a recent study by
Bartunov et al. (2018) evaluate variants of target-propagation (TP) and
feedback alignment (FA) on MINIST, CIFAR, and ImageNet datasets, and find that
although many of the proposed algorithms perform well on MNIST and CIFAR, they
perform significantly worse than BP on ImageNet. Here, we additionally evaluate
the sign-symmetry algorithm (Liao et al., 2016), which differs from both BP and
FA in that the feedback and feedforward weights share signs but not magnitudes.
We examine the performance of sign-symmetry and feedback alignment on ImageNet
and MS COCO datasets using different network architectures (ResNet-18 and
AlexNet for ImageNet, RetinaNet for MS COCO). Surprisingly, networks trained
with sign-symmetry can attain classification performance approaching that of
BP-trained networks. These results complement the study by Bartunov et al.
(2018), and establish a new benchmark for future biologically plausible
learning algorithms on more difficult datasets and more complex architectures.

研究了为了解决反向传播算法对称性问题的不同技术及其对不同数据集和网络结构的适用性，其中 sign-symmetry 算法展现出了逼近 BP 算法的分类性能。