Learning Ultra-Lean Classifiers with Structured Functions and Binary Lifting
ABOUT THIS PROJECT
At a glance
| PRINCIPAL INVESTIGATORS | RESEARCHERS | THEMES |
|---|---|---|
| Stella Yu | model compression, manifold learning, complex-valued deep learning, binary lifting |
Deploying classifiers optimally learned offline from big data requires model compression and quantization, often with lossy performance and yet not enough compression. Our idea is to train a lean structured model to begin with, by building in the type of invariance the data/task desires, the type of layer functions that enforces sharing, and by delivering a binary representation that can be executed using logic-only operations. An ultra-lean classifier can thus be efficiently learned from the start to the end and deployed with far fewer MACs than a conventional deep classifier network.