# Mixed Link Networks Source: [Mixed Link Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:55:10.976Z Authors: Wenhai Wang, Xiang Li, Jian Yang, Tong Lu Published: 2018-02-06T05:50:34.000Z ## Abstract Basing on the analysis by revealing the equivalence of modern networks, we find that both ResNet and DenseNet are essentially derived from the same "dense topology", yet they only differ in the form of connection -- addition (dubbed "inner link") vs. concatenation (dubbed "outer link"). However, both two forms of connections have the superiority and insufficiency. To combine their advantages and avoid certain limitations on representation learning, we present a highly efficient and modularized Mixed Link Network (MixNet) which is equipped with flexible inner link and outer link modules. Consequently, ResNet, DenseNet and Dual Path Network (DPN) can be regarded as a special case of MixNet, respectively. Furthermore, we demonstrate that MixNets can achieve superior efficiency in parameter over the state-of-the-art architectures on many competitive datasets like CIFAR-10/100, SVHN and ImageNet. ## 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) (Accuracy)](): Image Classification; CIFAR-10 (test); Accuracy: not recorded. - [Image Classification on ImageNet 2012 (val)](): Image Classification; ImageNet 2012 (val); Top-1 Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()