{"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/re-reproducibility-report-of-interpretable","title":"[Re] Reproducibility report of \"Interpretable Complex-Valued Neural Networks for Privacy Protection\"","arxiv_id":null,"date":"2021-01-31","proceeding":"RC 2020","authors":["Arsen Sheverdin","Noud Corten","Alko Knijff","Georg Lange"],"abstract":"Scope of Reproducibility\n\nThe original work by Xiang et al.1 claimed (1) that complex-valued DNNs effectively increase the difficulty of inferring inputs for the adversary attacks compared to the baseline. In addition, Xiang et al.1 stated that the (2) proposed privacy-protecting complexvalued DNN effectively preserves the accuracy when compared to the baseline.\n\nMethodology\n\nSince the original paperʼs code was not published, all of the codebase was written independently from scratch, based solemnly on how it was described in the paper. We mostly used a Nvidiaʼs RTX 2060 Super as the GPU and a AMD Ryzen 3600x as the CPU. The runtime of each model was highly dependant on the architecture used. The runtimes for each model can be found in Table 2.\n\nResults\n\nIn contrast to the first claim, we have discovered that for most of the architectures, reconstruction errors for the attacks are quite low, which means that in our models the first claim is not supported. We also found that for most of the models, the classification error is somewhat higher than those provided in the paper. However, these indeed relate to the original work and partially support the second claim of the authors.\n\nWhat was easy\n\nAuthors of the original paper utilized famous architectures for some of architecturesʼ parts, such as ResNet and LeNet, that were well explained and defined in the literature. In addition, authors, provided formulas on the modified rotation-invariant Complex DNN modules (ReLU, max pooling etc.), implementation of which was relatively straightforward. The paper was based on the openly available datasets.\n\nWhat was difficult\n\nThe paper did not provide any information on the architecture of the critic for the WGAN, along with the architecture of the angle discriminator utilized in inversion attack 1. It also does not provide any information about crucial hyperparameters, such as the k value used for k-anonimity.\n\nCommunication with original authors\n\nWe did not contact the original authors of the publication.","url_abs":"http://rescience.github.io/bibliography/Sheverdin_2021.html","url_pdf":"https://zenodo.org/record/4835431/files/article.pdf","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":"re-reproducibility-report-of-interpretable","repo_url":"https://github.com/GANA-FACT-AI/gana-fact-ai","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":null,"task_name":"GPU"}],"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":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"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":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"wgan","method_name":"WGAN"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}