  Weight and activation binarization can efficiently compress deep neural
networks and accelerate model inference, but cause severe accuracy degradation.
Existing optimization methods for binary neural networks (BNNs) focus on
fitting full-precision networks to reduce quantization errors, and suffer from
the trade-off between accuracy and computational complexity. In contrast,
considering the limited learning ability and information loss caused by the
limited representational capability of BNNs, we propose IR$^2$Net to stimulate
the potential of BNNs and improve the network accuracy by restricting the input
information and recovering the feature information, including: 1) information
restriction: for a BNN, by evaluating the learning ability on the input
information, discarding some of the information it cannot focus on, and
limiting the amount of input information to match its learning ability; 2)
information recovery: due to the information loss in forward propagation, the
output feature information of the network is not enough to support accurate
classification. By selecting some shallow feature maps with richer information,
and fusing them with the final feature maps to recover the feature information.
In addition, the computational cost is reduced by streamlining the information
recovery method to strike a better trade-off between accuracy and efficiency.
Experimental results demonstrate that our approach still achieves comparable
accuracy even with $ \sim $10x floating-point operations (FLOPs) reduction for
ResNet-18. The models and code are available at
https://github.com/pingxue-hfut/IR2Net.
