基于突触进化驱动遗传编码的深度神经网络进化合成
Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding
摘要：
最近，人们对实现高效的深度神经网络架构产生了极大的兴趣。实现这一目标的一个有希望的范例是进化深度智能的概念，它试图模仿生物进化过程，在连续几代人中合成高效的深度神经网络。进化深度智能的一个重要方面是用于模仿遗传的遗传编码方案，这会对后代深度神经网络的质量产生重大影响。出于神经生物学现象的突触集群，我们引入了一个新的遗传编码方案，突触概率被驱动朝向形成一个高度稀疏的突触簇。图像分类任务的实验结果表明，使用这种突触簇驱动的遗传编码方案的合成后代网络可以实现最先进的性能，同时具有不仅比原始祖先网络更有效的网络架构（MNIST的突触减少了约125倍），而且还为GPU加速的机器学习应用量身定制。
英文摘要：
There has been significant recent interest towards achieving highly efficient deep neural network architectures. A promising paradigm for achieving this is the concept of evolutionary deep intelligence, which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural networks over successive generations. An important aspect of evolutionary deep intelligence is the genetic encoding scheme used to mimic heredity, which can have a significant impact on the quality of offspring deep neural networks. Motivated by the neurobiological phenomenon of synaptic clustering, we introduce a new genetic encoding scheme where synaptic probability is driven towards the formation of a highly sparse set of synaptic clusters. Experimental results for the task of image classification demonstrated that the synthesized offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art performance while having network architectures that are not only significantly more efficient (with a ~125-fold decrease in synapses for MNIST) compared to the original ancestor network, but also tailored for GPU-accelerated machine learning applications.
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