A Neural Architecture Search Method using Auxiliary Evaluation Metric based on ResNet Architecture
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Abstract (arXiv)
This paper proposes a neural architecture search space using ResNet as a framework, with search objectives including parameters for convolution, pooling, fully connected layers, and connectivity of the residual network. In addition to recognition accuracy, this paper uses the loss value on the validation set as a secondary objective for optimization. The experimental results demonstrate that the search space of this paper together with the optimisation approach can find competitive network architectures on the MNIST, Fashion-MNIST and CIFAR100 datasets.
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References · 12
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
- 2014Going Deeper with Convolutionscited by 4,231
- 2019Single Path One-Shot Neural Architecture Search with Uniform Samplingcited by 411
- 2017Evolving Deep Convolutional Neural Networks for Image Classificationcited by 54
- 2021BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neuronscited by 22
- 2022PRE-NAS: Predictor-assisted Evolutionary Neural Architecture Searchcited by 8
- 2021BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Searchcited by 7
- 2022Evolution of Activation Functions for Deep Learning-Based Image Classificationcited by 6
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