Image Representations
Scattering Transform
Introduced by Bruna et al. in Invariant Scattering Convolution NetworksA wavelet scattering transform computes a translation invariant representation, which is stable to deformation, using a deep convolution network architecture. It computes non-linear invariants with modulus and averaging pooling functions. It helps to eliminate the image variability due to translation and is stable to deformations.
Image source: Bruna and Mallat
Source: Invariant Scattering Convolution Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Classification | 2 | 16.67% |
| Explainable artificial intelligence | 1 | 8.33% |
| Explainable Artificial Intelligence (XAI) | 1 | 8.33% |
| Self-Supervised Learning | 1 | 8.33% |
| Clustering | 1 | 8.33% |
| Deep Clustering | 1 | 8.33% |
| Image Clustering | 1 | 8.33% |
| Decoder | 1 | 8.33% |
| Image Generation | 1 | 8.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
