Combating Catastrophic Forgetting
Developed a novel Weighted Prototype Update for a CIFAR-10 classifier.
- Developed a novel Weighted Prototype Update for a CIFAR-10 classifier (on BeiT features) that generalizes to same-distribution datasets without labels
- Prevented catastrophic forgetting, limiting worst-case accuracy to 97.4%
- Designed a Clustering-based Mean Shift prototype update algorithm for Unsupervised Domain Adaptation
- Achieved ~5% accuracy improvement on out-of-distribution data while maintaining performance on prior data