LARS
Introduced by You et al. in Large Batch Training of Convolutional NetworksLayer-wise Adaptive Rate Scaling, or LARS, is a large batch optimization technique. There are two notable differences between LARS and other adaptive algorithms such as Adam or RMSProp: first, LARS uses a separate learning rate for each layer and not for each weight. And second, the magnitude of the update is controlled with respect to the weight norm for better control of training speed.
$$m_{t} = \beta_{1}m_{t-1} + \left(1-\beta_{1}\right)\left(g_{t} + \lambda{x_{t}}\right)$$ $$x_{t+1}^{\left(i\right)} = x_{t}^{\left(i\right)} - \eta_{t}\frac{\phi\left(|| x_{t}^{\left(i\right)} ||\right)}{|| m_{t}^{\left(i\right)} || }m_{t}^{\left(i\right)} $$
Source: Large Batch Training of Convolutional Networks
Latest Papers
Tasks
| TASK | PAPERS | SHARE |
|---|---|---|
| Image Classification | 7 | 31.82% |
| Self-Supervised Learning | 4 | 18.18% |
| Self-Supervised Image Classification | 3 | 13.64% |
| Semi-Supervised Image Classification | 3 | 13.64% |
| Question Answering | 2 | 9.09% |
| Continual Learning | 1 | 4.55% |
| Image Retrieval | 1 | 4.55% |
| Learning-To-Rank | 1 | 4.55% |
Usage Over Time
Components
| COMPONENT | TYPE |
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|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |