Ashok Veeraraghavan

Learning from Noisy Web Data with Category-Level Supervision

2018

Learning from web data is increasingly popular due to abundant free web resources. However, the performance gap between webly supervised learning and traditional supervised learning is still very large, due to the label noise of …

Learning Transferable Features for Implicit Neural Representations

2024 · arXiv (Cornell University)

Implicit neural representations (INRs) have demonstrated success in a variety of applications, including inverse problems and neural rendering. An INR is typically trained to capture one signal of interest, resulting in learned neural features that …