CSCO: Connectivity Search of Convolutional Operators

• URL: http://arxiv.org/abs/2404.17152v1
• Date: Fri, 26 Apr 2024 04:52:45 GMT
• Title: CSCO: Connectivity Search of Convolutional Operators
• Authors: Tunhou Zhang, Shiyu Li, Hsin-Pai Cheng, Feng Yan, Hai Li, Yiran Chen,
• Abstract summary: We propose CSCO, a novel paradigm that fabricates effective connectivity of convolutional operators. CSCO guides the exploration via a neural predictor as a surrogate of the ground-truth performance. Results on ImageNet show 0.6% performance improvement over hand-crafted and NAS-crafted dense connectivity.
• Score: 12.928148870505375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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