  Traditional deep neural networks typically use end-to-end backpropagation,
which often places a big burden on GPU memory. Another promising training
method is local learning, which involves splitting the network into blocks and
training them in parallel with the help of an auxiliary network. Local learning
has been widely studied and applied to image classification tasks, and its
performance is comparable to that of end-to-end method. However, different
image tasks often rely on different feature representations, which is difficult
for typical auxiliary networks to adapt to. To solve this problem, we propose
the construction method of Global-Local Collaborative Auxiliary Network
(GLCAN), which provides a macroscopic design approach for auxiliary networks.
This is the first demonstration that local learning methods can be successfully
applied to other tasks such as object detection and super-resolution. GLCAN not
only saves a lot of GPU memory, but also has comparable performance to an
end-to-end approach on data sets for multiple different tasks.
