This research introduces HALO-Loss, a novel method for training neural networks to abstain from making predictions when uncertain. It allows models to express "I don't know" rather than providing potentially incorrect answers, improving reliability.
!["I don't know!": Teaching neural networks to abstain with the HALO-Loss. [R]](/cdn-cgi/image/width=3840,quality=75,format=webp/https://preview.redd.it/loxsfywek4vg1.png?width=140&height=123&auto=webp&s=e494bc934351aeddad8b50b76059d35dc7987a4f)
This content introduces a novel loss function for training deep neural networks, based on the Squared Earth Mover's Distance. It aims to enhance the effectiveness of deep learning models by providing a more robust measure for comparing probability distributions.
This paper introduces Soft Silhouette Loss, a novel differentiable objective for deep learning, inspired by the classical silhouette coefficient. It aims to learn discriminative representations by enforcing intra-class compactness and inter-class separation more efficiently than existing metric learning approaches.
This article details the learning process of neural networks through optimization, covering the training loop from forward propagation to weight updates. It explains the significance of backpropagation and loss functions in computing gradients and adjusting model parameters.