Combining Adam and its Inverse Counterpart to Enhance Generalization of Deep Learning Optimizers
In the training of neural networks, adaptive moment estimation (Adam) typically converges fast but exhibits suboptimal generalization performance. A widely accepted explanation for its defect in generalization is that it often tends to converge to sharp minima. To enhance its ability to find flat minima, we propose its new variant named inverse Adam (InvAdam). The key improvement of InvAdam lies in its parameter update mechanism, which is opposite to that of Adam. Specifically, it computes element-wise multiplication of the first-order and second-order moments, while Adam computes the element-wise division of these two moments. This modification aims to increase the step size of the param...
| Authors | Tao Shi, Liangming Chen, Long Jin et al. |
|---|---|
| Primary category | cs.LG |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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