3 citations · 5 across the 10 of their papers we have counts for
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LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling
Abhishek Moturu, Babak Taati, Anna Goldenberg
Label noise is common in medical imaging datasets due to factors such as inter-rater variability, annotation errors, and ambiguous cases. This can severely undermine the reliabilit…
LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection
Abhishek Moturu, Anna Goldenberg, Babak Taati
Synthetic data is useful only when the added samples fill missing parts of the training distribution that matter for the downstream task. We introduce LiBaGS, a lightweight, genera…
LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training
Abhishek Moturu, Muhammad Muzammil, Anna Goldenberg +1
Training deep neural networks with noise and data heterogeneity is a major challenge. We introduce Lightweight Learnable Adaptive Weighting (LiLAW), a method that dynamically adjus…