# Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting Source: [Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:43:17.087Z Authors: Anders Oland, Aayush Bansal, Roger B. Dannenberg, Bhiksha Raj Published: 2017-07-13T16:19:09.000Z ## Abstract In this work, we show that saturating output activation functions, such as the softmax, impede learning on a number of standard classification tasks. Moreover, we present results showing that the utility of softmax does not stem from the normalization, as some have speculated. In fact, the normalization makes things worse. Rather, the advantage is in the exponentiation of error gradients. This exponential gradient boosting is shown to speed up convergence and improve generalization. To this end, we demonstrate faster convergence and better performance on diverse classification tasks: image classification using CIFAR-10 and ImageNet, and semantic segmentation using PASCAL VOC 2012. In the latter case, using the state-of-the-art neural network architecture, the model converged 33% faster with our method (roughly two days of training less) than with the standard softmax activation, and with a slightly better performance to boot. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()