Robust Training
Deep Equilibrium Models
Introduced by Bai et al. in Deep Equilibrium ModelsA new kind of implicit models, where the output of the network is defined as the solution to an "infinite-level" fixed point equation. Thanks to this we can compute the gradient of the output without activations and therefore with a significantly reduced memory footprint.
Source: Deep Equilibrium ModelsPapers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Language Modelling | 3 | 10.71% |
| Adversarial Robustness | 3 | 10.71% |
| Denoising | 3 | 10.71% |
| Object Detection | 2 | 7.14% |
| Adversarial Defense | 2 | 7.14% |
| Image Reconstruction | 2 | 7.14% |
| Image Restoration | 1 | 3.57% |
| Decoder | 1 | 3.57% |
| Benchmarking | 1 | 3.57% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
