Stochastic Optimization
Gradient Checkpointing
Introduced by Chen et al. in Training Deep Nets with Sublinear Memory CostGradient Checkpointing is a method used for reducing the memory footprint when training deep neural networks, at the cost of having a small increase in computation time.
Source: Training Deep Nets with Sublinear Memory Cost
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 2 | 11.76% |
| Mamba | 1 | 5.88% |
| Video Understanding | 1 | 5.88% |
| Computational Efficiency | 1 | 5.88% |
| Music Generation | 1 | 5.88% |
| Diversity | 1 | 5.88% |
| Image Captioning | 1 | 5.88% |
| Machine Translation | 1 | 5.88% |
| MRI Reconstruction | 1 | 5.88% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
