ϵ-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise Jialiang Wang 1 Xiong Zhou 1 Deming Zhai

Noisy labels pose a common challenge for training accurate deep neural networks. To mitigate label noise, prior studies have proposed various robust loss functions to achieve noise tolerance in the presence of label noise, particularly symmetric losses.