Information Bottleneck
Aggregated Learning
Introduced by Soflaei et al. in Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network ClassifiersAggregated Learning (AgrLearn) is a vector-quantization approach to learning neural network classifiers. It builds on an equivalence between IB learning and IB quantization and exploits the power of vector quantization, which is well known in information theory.
Source: Aggregated Learning: A Vector-Quantization Approach to Learning Neural Network Classifiers
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Classification | 1 | 25.00% |
| General Classification | 1 | 25.00% |
| Quantization | 1 | 25.00% |
| Text Classification | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
