{"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/api-net-robust-generative-classifier-via-a","title":"API-Net: Robust Generative Classifier via a Single Discriminator","arxiv_id":null,"date":"2020-08-01","proceeding":"ECCV 2020 8","authors":["Xinshuai Dong","Hong Liu","Rongrong Ji","Liujuan Cao","Qixiang Ye","Jianzhuang Liu","Qi Tian"],"abstract":"Robustness of deep neural network classifiers has been attracting increased attention. As for the robust classification problem, a generative classifier typically models the distribution of inputs and labels, and thus can better handle off-manifold examples at the cost of a concise structure. On the contrary, a discriminative classifier only models the conditional distribution of labels given inputs, but benefits from effective optimization owing to its succinct structure. This work aims for a solution of generative classifiers that can profit from the merits of both. To this end, we propose an Anti-Perturbation Inference (API) method, which searches for anti-perturbations to maximize the lower bound of the joint log-likelihood of inputs and classes. By leveraging the lower bound to approximate Bayes' rule, we construct a generative classifier Anti-Perturbation Inference Net (API-Net) upon a single discriminator. It takes advantage of the generative properties to tackle off-manifold examples while maintaining a succinct structure for effective optimization. Experiments show that API successfully neutralizes adversarial perturbations, and API-Net consistently outperforms state-of-the-art defenses on prevailing benchmarks, including CIFAR-10, MNIST, and SVHN.","url_abs":"https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1895_ECCV_2020_paper.php","url_pdf":"https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580375.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":"api-net-robust-generative-classifier-via-a","repo_url":"https://github.com/dongxinshuai/API-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"robust-classification","task_name":"Robust classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}