Binary Neural Networks (BNNs), neural networks with weights and activations
constrained to -1(0) and +1, are an alternative to deep neural networks which
offer faster training, lower memory consumption and lightweight models, ideal
for use in resource constrained devices while being able to utilize the
architecture of their deep neural network counterpart. However, the input
binarization step used in BNNs causes a severe accuracy loss. In this paper, we
introduce a novel hybrid neural network architecture, Hybrid Binary Neural
Network (HyBNN), consisting of a task-independent, general, full-precision
variational autoencoder with a binary latent space and a task specific binary
neural network that is able to greatly limit the accuracy loss due to input
binarization by using the full precision variational autoencoder as a feature
extractor. We use it to combine the state-of-the-art accuracy of deep neural
networks with the much faster training time, quicker test-time inference and
power efficiency of binary neural networks. We show that our proposed system is
able to very significantly outperform a vanilla binary neural network with
input binarization. We also introduce FedHyBNN, a highly communication
efficient federated counterpart to HyBNN and demonstrate that it is able to
reach the same accuracy as its non-federated equivalent. We make our source
code, experimental parameters and models available at:
https://anonymous.4open.science/r/HyBNN.

本文介绍了一种新颖的混合神经网络结构，Hybrid Binary Neural Network（HyBNN），其由一个任务无关的、通用的、全精度变分自动编码器和一个任务特定的二进制神经网络组成，它能够通过使用全精度变分自动编码器作为特征提取器大大限制输入二元化引起的准确性损失，结合深度神经网络的最先进的准确性以及二进制神经网络的更快的训练时间、更快的测试时间推理和更高的功率效率。我们展示了我们提出的系统能够明显优于常规的二进制神经网络。此外，我们还介绍了 FedHyBNN，这是 HyBNN 的高度通信效率的联邦学习版本，并证明它能够达到与其非联邦版本相同的准确性。