Exploring dense connectivity of convolutional operators establishes critical
"synapses" to communicate feature vectors from different levels and enriches
the set of transformations on Computer Vision applications. Yet, even with
heavy-machinery approaches such as Neural Architecture Search (NAS),
discovering effective connectivity patterns requires tremendous efforts due to
either constrained connectivity design space or a sub-optimal exploration
process induced by an unconstrained search space. In this paper, we propose
CSCO, a novel paradigm that fabricates effective connectivity of convolutional
operators with minimal utilization of existing design motifs and further
utilizes the discovered wiring to construct high-performing ConvNets. CSCO
guides the exploration via a neural predictor as a surrogate of the
ground-truth performance. We introduce Graph Isomorphism as data augmentation
to improve sample efficiency and propose a Metropolis-Hastings Evolutionary
Search (MH-ES) to evade locally optimal architectures and advance search
quality. Results on ImageNet show ~0.6% performance improvement over
hand-crafted and NAS-crafted dense connectivity. Our code is publicly
available.

通过最小化利用现有设计模式来创建卷积算子的有效连接，并利用发现的连接构建高性能的 ConvNets，本文提出了 CSCO，一个新颖的范例，通过神经预测器作为地面真实性能的代理引导搜索，引入图同构的数据增强来提高样本效率，并提出了 Metropolis-Hastings 进化搜索 (MH-ES) 来避免局部最优结构并提高搜索质量。在 ImageNet 上的结果显示，与手工和 NAS 制作的密集连接相比，性能提高了约 0.6%。