juntang-zhuang/Adabelief-Optimizer

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1070 stars · 107 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

AdaBelief is a deep learning optimizer that adapts step sizes based on the 'belief' in observed gradients, combining Adam's fast convergence with SGD's generalization. It provides pip-installable packages for both PyTorch (adabelief-pytorch) and TensorFlow (adabelief-tf), published as a NeurIPS 2020 Spotlight paper.

Use cases

• train neural networks with an optimizer that generalizes like SGD
• find a fast-converging optimizer for GAN training
• replace Adam in image classification experiments
• train transformers with a stable adaptive optimizer
• use a drop-in optimizer for reinforcement learning agents
• benchmark alternative optimizers against Adam and SGD

When to choose

• you want Adam-like speed with better generalization in PyTorch or TensorFlow
• you are training GANs where Adam is unstable
• you need a well-cited, research-backed optimizer with reference hyperparameters

When to avoid

• you need a heavily maintained production library with frequent updates
• your framework is not PyTorch or TensorFlow
• you require optimizers beyond AdaBelief in one package

Facets

library · maturity stable

machine-learning deep-learning machine-learning deep-learning artificial-intelligence python cross-platform optimizer pytorch tensorflow adabelief gradient-descent neurips-2020 research

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