Image Feature Extractors
Hamburger
Introduced by Geng et al. in Is Attention Better Than Matrix Decomposition?Hamburger is a global context module that employs matrix decomposition to factorize the learned representation into sub-matrices so as to recover the clean low-rank signal subspace. The key idea is, if we formulate the inductive bias like the global context into an objective function, the optimization algorithm to minimize the objective function can construct a computational graph, i.e., the architecture we need in the networks.
Source: Is Attention Better Than Matrix Decomposition?
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Conditional Image Generation | 1 | 33.33% |
| Image Generation | 1 | 33.33% |
| Semantic Segmentation | 1 | 33.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
