Adaptive-k: A simple and effective method for robust training in label noisy datasets
Training deep learning models on large datasets is essential for their success; however, these datasets often contain label noise, which can significantly decrease the classification performance on test datasets. To address this issue, a research team consisting of Enes Dedeoglu, H. Toprak Kesgin, and Prof. Dr. M. Fatih Amasyali from Yildiz Technical University developed a groundbreaking method called Adaptive-k, which improves the optimization process and yields better results in the presence of label noise. Their research was published on 15 August 2024 in ...













