{"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/model-free-energy-distance-for-pruning-dnns","title":"Model-Free Energy Distance for Pruning DNNs","arxiv_id":null,"date":"2021-01-01","proceeding":null,"authors":["Mohammadreza Soltani","Suya Wu","Yuerong Li","Jie Ding","Vahid Tarokh"],"abstract":"We propose a novel method for compressing Deep Neural Networks (DNNs) with competitive performance to state-of-the-art methods. We measure a new model-free information between the feature maps and the output of the network. Model-freeness of our information measure guarantees that no parametric assumptions on the feature distribution are required. The new model-free information is subsequently used to prune a collection of redundant layers in the networks with skip-connections. Numerical experiments on CIFAR-10/100, SVHN, Tiny ImageNet, and ImageNet data sets show the efficacy of the proposed approach in compressing deep models. For instance, in classifying CIFAR-10 images our method achieves respectively 64.50% and 60.31% reduction in the number of parameters and FLOPs for a full DenseNet model with 0.77 million parameters while dropping only 1% in the test accuracy. Our code is available at https://github.com/suuyawu/PEDmodelcompression","url_abs":"https://openreview.net/forum?id=k2TyMLwuikx","url_pdf":"https://openreview.net/pdf?id=k2TyMLwuikx","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"model-free-energy-distance-for-pruning-dnns","repo_url":"https://github.com/suuyawu/PEDmodelcompression","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-block","method_name":"Dense Block"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}