Search Results for author:
Found 4 papers, 0 papers with code
T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning
The key idea of T-CIL is to perturb exemplars more strongly for old tasks than for the new task by adjusting the perturbation direction based on feature distance, with the single magnitude determined using the new-task validation set.
RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks
We study the problem of robust data augmentation for regression tasks in the presence of noisy data.
Quilt: Robust Data Segment Selection against Concept Drifts
However, we contend that explicitly utilizing the drifted data together leads to much better model accuracy and propose Quilt, a data-centric framework for identifying and selecting data segments that maximize model accuracy.
RegMix: Data Mixing Augmentation for Regression
Data augmentation is becoming essential for improving regression performance in critical applications including manufacturing, climate prediction, and finance.
