Year: 2020
Type: conference-paper
Abstract: Finding an early stopping point at maximum obtainable test accuracy (MOTA) is a challenging problem when training deep neural networks (DNNs) with noisy labeled data. Recent studies assume either that i) a clean validation set is available or ii) the noise ratio is known, or, both. However, often a clean validation set is unavailable, and the noise estimation can be inaccurate. To overcome these issues, we provide a novel training solution, free of these conditions. We analyze the rate of change of the training accuracy under different conditions to identify a training stop region. We further develop a heuristic algorithm (AutoTSP) to find a training stop point (TSP) at or close to MOTA. We validate the robustness of AutoTSP through several experiments on various datasets, noise ratios and architectures.
Authors: Sree Ram Kamabattula, Venkat Devarajan, Babak Namazi, Ganesh Sankaranarayanan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3118094271