Binarized Neural Networks for Resource-Efficient Hashing with Minimizing
Quantization Loss
Abstract
In order to solve the problem of memory consumption and computational requirements, this paper proposes a novel learning binary neural network framework to achieve a resource-efficient
deep hashing. In contrast to floating-point (32-
bit) full-precision networks, the proposed method
achieves a 32x model compression rate. At the
same time, computational burden in convolution
is greatly reduced due to efficient Boolean operations. To this end, in our framework, a new quantization loss defined between the binary weights
and the learned real values is minimized to reduce the model distortion, while, by minimizing a
binary entropy function, the discrete optimization
is successfully avoided and the stochastic gradient descend method can be used smoothly. More
importantly, we provide two theories to demonstrate the necessity and effectiveness of minimizing the quantization losses for both weights and
activations. Numerous experiments show that the
proposed method can achieve fast code generation
without sacrificing accuracy