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CONetV2: Efficient Auto-Channel Size Optimization for CNNs
[article]
2021
arXiv
pre-print
Neural Architecture Search (NAS) has been pivotal in finding optimal network configurations for Convolution Neural Networks (CNNs). While many methods explore NAS from a global search-space perspective, the employed optimization schemes typically require heavy computational resources. This work introduces a method that is efficient in computationally constrained environments by examining the micro-search space of channel size. In tackling channel-size optimization, we design an automated
arXiv:2110.06830v1
fatcat:frn4xbuuubd73dqj37h3sz4bzm