A data-aware dictionary-learning based technique for the acceleration of deep convolutional networks
Erion‐Vasilis Pikoulis, Christos Mavrokefalidis, Aris S. Lalos · 2021
The deployment of high performing deep learning models on platforms of limited resources is currently an active area of research. Among the main directions followed so far, pre-trained neural networks are accelerated and compressed by appropriately modifying their structure and / or parameters. Capitalizing on a recently proposed codebook of a special structure that can be utilized in the frame of the so-called weight sharing methods, this paper describes a "data-driven" technique for designing such a codebook. The performance of the technique, in terms of the observed representation error and classification accuracy versus the achieved acceleration ratio, is demonstrated by considering the VGG16 and the ResNet18 models, pre-trained on the ILSVRC2012 dataset.