4 ms·
1. Try to get more data 2. Augmentation 3. Heavy regularization 4. Cross-validation 5. Transfer learning/fine-tuning from related data 6. Sharpness aware o
by zone411 4y ago
1. Try to get more data
2. Augmentation
3. Heavy regularization
4. Cross-validation
5. Transfer learning/fine-tuning from related data
6. Sharpness aware optimization
7. Ensembles
8. Any domain knowledge/feature engineering
- rexreed 4y ago9. Synthetic data 10. Single-shot / low-shot learning methods 11. Reducing the scope of the problem so you don't even need that much data 12. Iterative model approaches that use submodels focused on different target problems (instead of trying to boil the ocean with a single model) A lot of these are not what you do, but how you do it, something popularized by CPMAI methodology. I would also add that I'm not sure the OP was tongue-in-cheek when saying that AGI is coming, so we don't need to worry about low data requirements. But needless to say, AGI is not coming any time soon, and if OpenAI's strategy for AGI is to be believed, it's anything but a low-data method. Unless you're counting on them to build the super-sized model that meets all needs.