Effectiveness of Optimization Algorithms in Deep Image Classification
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Abstract (arXiv)
Adam is applied widely to train neural networks. Different kinds of Adam methods with different features pop out. Recently two new adam optimizers, AdaBelief and Padam are introduced among the community. We analyze these two adam optimizers and compare them with other conventional optimizers (Adam, SGD + Momentum) in the scenario of image classification. We evaluate the performance of these optimization algorithms on AlexNet and simplified versions of VGGNet, ResNet using the EMNIST dataset. (Benchmark algorithm is available at hyperref[https://github.com/chuiyunjun/projectCSC413]https://github.com/chuiyunjun/projectCSC413).
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References · 8
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2016An overview of gradient descent optimization algorithmscited by 1,033
- 2020AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradientscited by 283
- 2017EMNIST: an extension of MNIST to handwritten letterscited by 213
- 2018Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networkscited by 90
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