{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/exploring-weight-symmetry-in-deep-neural","title":"Exploring Weight Symmetry in Deep Neural Networks","arxiv_id":"1812.11027","date":"2018-12-28","proceeding":null,"authors":["Xu Shell Hu","Sergey Zagoruyko","Nikos Komodakis"],"abstract":"We propose to impose symmetry in neural network parameters to improve\nparameter usage and make use of dedicated convolution and matrix multiplication\nroutines. Due to significant reduction in the number of parameters as a result\nof the symmetry constraints, one would expect a dramatic drop in accuracy.\nSurprisingly, we show that this is not the case, and, depending on network\nsize, symmetry can have little or no negative effect on network accuracy,\nespecially in deep overparameterized networks. We propose several ways to\nimpose local symmetry in recurrent and convolutional neural networks, and show\nthat our symmetry parameterizations satisfy universal approximation property\nfor single hidden layer networks. We extensively evaluate these\nparameterizations on CIFAR, ImageNet and language modeling datasets, showing\nsignificant benefits from the use of symmetry. For instance, our ResNet-101\nwith channel-wise symmetry has almost 25% less parameters and only 0.2%\naccuracy loss on ImageNet. 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