AdaFamily: A family of Adam-like adaptive gradient methods
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Abstract (arXiv)
We propose AdaFamily, a novel method for training deep neural networks. It is a family of adaptive gradient methods and can be interpreted as sort of a blend of the optimization algorithms Adam, AdaBelief and AdaMomentum. We perform experiments on standard datasets for image classification, demonstrating that our proposed method outperforms these algorithms.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 14 references that are in the map. First posted on arXiv 3 Mar 2022.
Builds on
7 of 14 references.
References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2016Densely Connected Convolutional Networkscited by 6,691
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
- 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
- 2020AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradientscited by 282
- 2020Descending through a Crowded Valley - Benchmarking Deep Learning Optimizerscited by 118
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Citing papers per year: 2023: 1.
1 paper.