# Deep Residual Networks with Exponential Linear Unit Source: [Deep Residual Networks with Exponential Linear Unit]() Collection index: [llms.txt]() Data updated: 2026-01-04T02:48:00.208Z Authors: Anish Shah, Eashan Kadam, Hena Shah, Sameer Shinde, Sandip Shingade Published: 2016-04-14T11:09:09.000Z ## Abstract Very deep convolutional neural networks introduced new problems like vanishing gradient and degradation. The recent successful contributions towards solving these problems are Residual and Highway Networks. These networks introduce skip connections that allow the information (from the input or those learned in earlier layers) to flow more into the deeper layers. These very deep models have lead to a considerable decrease in test errors, on benchmarks like ImageNet and COCO. In this paper, we propose the use of exponential linear unit instead of the combination of ReLU and Batch Normalization in Residual Networks. We show that this not only speeds up learning in Residual Networks but also improves the accuracy as the depth increases. It improves the test error on almost all data sets, like CIFAR-10 and CIFAR-100 ## 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. - [Image Classification on CIFAR-100 (test)](): Image Classification; CIFAR-100 (test); Accuracy: not recorded. - [Image Classification on CIFAR-10 (test)](): Image Classification; CIFAR-10 (test); Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()