{"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/quality-agnostic-image-recognition-via","title":"Quality-Agnostic Image Recognition via Invertible Decoder","arxiv_id":null,"date":"2021-06-19","proceeding":"CVPR 2021 1","authors":["Insoo Kim","Seungju Han","Ji-won Baek","Seong-Jin Park","Jae-Joon Han","Jinwoo Shin"],"abstract":"    Despite the remarkable performance of deep models on image recognition tasks, they are known to be susceptible to common corruptions such as blur, noise, and low-resolution. Data augmentation is a conventional way to build a robust model by considering these common corruptions during the training. However, a naive data augmentation scheme may result in a non-specialized model for particular corruptions, as the model tends to learn the averaged distribution among corruptions. To mitigate the issue, we propose a new paradigm of training deep image recognition networks that produce clean-like features from any quality image via an invertible neural architecture. The proposed method consists of two stages. In the first stage, we train an invertible network with only clean images under the recognition objective. In the second stage, its inversion, i.e., the invertible decoder, is attached to a new recognition network and we train this encoder-decoder network using both clean and corrupted images by considering recognition and reconstruction objectives. Our two-stage scheme allows the network to produce clean-like and robust features from any quality images, by reconstructing their clean images via the invertible decoder. We demonstrate the effectiveness of our method on image classification and face recognition tasks.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2021/html/Kim_Quality-Agnostic_Image_Recognition_via_Invertible_Decoder_CVPR_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2021/papers/Kim_Quality-Agnostic_Image_Recognition_via_Invertible_Decoder_CVPR_2021_paper.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":"quality-agnostic-image-recognition-via","repo_url":"https://github.com/tjdhg456/QualNet-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-generalization-on-imagenet-c","task":"Domain Generalization","dataset":"ImageNet-C","model":"QualNet (ResNeXt101)","rank_in_archive_order":17,"of":47,"metrics":{"mean Corruption Error (mCE)":"42.5"},"uses_additional_data":false},{"leaderboard":"/sota/domain-generalization-on-imagenet-c","task":"Domain Generalization","dataset":"ImageNet-C","model":"QualNet (ResNet-50)","rank_in_archive_order":28,"of":47,"metrics":{"mean Corruption Error (mCE)":"50.6"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}