Compact Image-Style Transfer: Channel Pruning on the Single Training of a Network

Abstract

Recent image-style transfer methods use the structure of a VGG feature network to encode and decode the feature map of the image. Since the network is designed for the general image-classification task, it has a number of channels and, accordingly, requires a huge amount of memory and high computational power, which is not mandatory for such a relatively simple task as image-style transfer. In this paper, we propose a new technique to size down the previously used style transfer network for eliminating the redundancy of the VGG feature network in memory consumption and computational cost. Our method automatically finds a number of consistently inactive convolution channels during the network training phase by using two new losses, i.e., channel loss and xor loss. The former maximizes the number of inactive channels and the latter fixes the positions of these inactive channels to be the same for the image. Our method improves the image generation speed to be up to 49% faster and reduces the number of parameters by 20% while maintaining style transferring performance. Additionally, our losses are also effective in pruning the VGG16 classifier network, i.e., parameter reduction by 26% and top-1 accuracy improvement by 0.16% on CIFAR-10.

A Compositional Transformer Based Autoencoder for Image Style Transfer

“…In these methods, CNNs are used to extract both high-level content features and stylized texture features. Starting from a random noise-based generated image, an iterative optimization is applied to align high-level semantic features of a content image based on a pre-trained VGG model [15] and style feature correlations at different channels represented by Gram Matrix. Their method improves the image generation speed to be up to 49% faster and reduces the number of parameters by 20% while maintaining style transferring performance.…”

Chaotic medical image encryption method using attention mechanism fusion ResNet model

“…Deep learning represents a significant advancement and extension of machine learning, offering enhanced capabilities for feature expression. With the continuous improvement in computational power, deep learning has demonstrated promising outcomes in various domains, including image processing (Lu et al, 2023 ), image steganalysis (Ge et al, 2021 ), image style transfer (Kim and Choi, 2022 ), and image reconstruction (Noda et al, 2023 ). Deep learning models can construct feature extractors with superior performance by leveraging large-scale datasets.…”