# AdaFamily: A family of Adam-like adaptive gradient methods

Source: [AdaFamily: A family of Adam-like adaptive gradient methods](<https://www.sota2.com/research/paper/68fa59081e209131ce9b3843>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T15:40:25.149Z

Authors: Hannes Fassold

Published: 2022-03-03T09:57:50.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2203.01603>)

[PDF](<https://arxiv.org/pdf/2203.01603.pdf>)