Abstract Despite great effectiveness of very deep and wide Con- volutional Neural Networks(CNNs)in various computer vi- sion tasks,the significant cost in terms of storage require- ment of such networks impedes the deployment on compu- tationally limited devices.In this paper,we propose new modulated convolutional networks(MCNs)to improve the portability of CNNs via binarized filters.In MCNs,we pro- pose a new loss function which considers the filter loss, center loss and softmax loss in an end-to-end framework. We first introduce modulation filters(M-Filters)to recover the unbinarized filters,which leads to a new architecture to calculate the network model.The convolution operation is further appro.ximated by considering intra-class compact- ness in the loss function.As a result,our MCNs can reduce the size of required storage space of convolutional filters by a factor of 32,in contrast to the fuull-precision model, while achieving much better performances than state-of the-art binarized models.Most importantly,MCNs achieve a comparable performance to the fiull-precision Resnets and WideResnets.The code will be available publicly soon.