Searching for Accurate Binary Neural Architectures
arXiv:1909.07378
Abstract
Binary neural networks have attracted tremendous attention due to the efficiency for deploying them on mobile devices. Since the weak expression ability of binary weights and features, their accuracy is usually much lower than that of full-precision (i.e. 32-bit) models. Here we present a new frame work for automatically searching for compact but accurate binary neural networks. In practice, number of channels in each layer will be encoded into the search space and optimized using the evolutionary algorithm. Experiments conducted on benchmark datasets and neural architectures demonstrate that our searched binary networks can achieve the performance of full-precision models with acceptable increments on model sizes and calculations.
arXiv commentAccepted by ICCV 2019 Neural Architects Workshop
References in corpus (9)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Distilling the Knowledge in a Neural Network
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Neural Architecture Search with Reinforcement Learning
- Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
- DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
- WRPN: Wide Reduced-Precision Networks
- Attribute Aware Pooling for Pedestrian Attribute Recognition
- Towards Evolutional Compression