Training Bit Fully Convolutional Network for Fast Semantic Segmentation

Understand

Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation.

• However, a typical FCN usually requires lots of floating point computation and large run-time memory, which effectively limits its usability.
• We propose a method to train Bit Fully Convolution Network (BFCN), a fully convolutional neural network that has low bit-width weights and activations.
• Because most of its computation-intensive convolutions are accomplished between low bit-width numbers, a BFCN can be accelerated by an efficient bit-convolution implementation.