{"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/scalable-learning-with-incremental","title":"Scalable Learning with Incremental Probabilistic PCA","arxiv_id":null,"date":"2022-12-20","proceeding":"IEEE International Conference on Big Data 2022 12","authors":["Boshi Wang","Adrian Barbu"],"abstract":"Incremental class learning is the classification problem of learning a model where instances from new object classes are added sequentially, and it is desired that the model be retrained only on the new classes with minimal training on the old classes. \r\nOne major problem facing class incremental learning is catastrophic forgetting, where the updated model forgets the old classes and focuses only on the new classes.\r\nThis paper proposes a simple and novel incremental class learning method that uses a self-supervised pretrained feature extractor to obtain meaningful features and trains Probabilistic PCA models on the extracted features for each class separately. \r\nThe Mahalanobis distance is used to obtain the classification result, and an equivalent equation is derived to make the approach computationally affordable.\r\nExperiments on standard and large datasets show that the proposed approach outperforms existing state of the art incremental learning methods by a large margin.\r\nThe fact that the model is trained on each class separately makes it applicable to training on very large datasets such as the whole ImageNet with more than 10,000 classes.","url_abs":"https://ieeexplore.ieee.org/document/10020330","url_pdf":"https://ani.stat.fsu.edu/~abarbu/papers/2022-Wang-Incremental_PPCA-BigData.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":"scalable-learning-with-incremental","repo_url":"https://github.com/barbua/PPCA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"class-incremental-learning","task_name":"Class Incremental Learning"},{"task_slug":"incremental-learning","task_name":"Incremental Learning"},{"task_slug":"class-incremental-learning-1","task_name":"class-incremental learning"}],"methods":[{"method_slug":"pca","method_name":"PCA"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/class-incremental-learning-on-cifar-100-50-1","task":"Class Incremental Learning","dataset":"CIFAR-100 - 50 classes + 10 steps of 5 classes","model":"PPCA-SWSL","rank_in_archive_order":1,"of":2,"metrics":{"Final Accuracy":"77.07"},"uses_additional_data":true},{"leaderboard":"/sota/class-incremental-learning-on-cifar-100-50-1","task":"Class Incremental Learning","dataset":"CIFAR-100 - 50 classes + 10 steps of 5 classes","model":"PPCA-CLIP","rank_in_archive_order":2,"of":2,"metrics":{"Final Accuracy":"69.71"},"uses_additional_data":true},{"leaderboard":"/sota/class-incremental-learning-on-cifar-100-50-2","task":"Class Incremental Learning","dataset":"CIFAR-100 - 50 classes + 5 steps of 10 classes","model":"PPCA-SWSL","rank_in_archive_order":1,"of":2,"metrics":{"Final Accuracy":"77.07"},"uses_additional_data":true},{"leaderboard":"/sota/class-incremental-learning-on-cifar-100-50-2","task":"Class Incremental Learning","dataset":"CIFAR-100 - 50 classes + 5 steps of 10 classes","model":"PPCA-CLIP","rank_in_archive_order":2,"of":2,"metrics":{"Final Accuracy":"69.71"},"uses_additional_data":true},{"leaderboard":"/sota/incremental-learning-on-cifar-100-50-classes-3","task":"Incremental Learning","dataset":"CIFAR-100 - 50 classes + 5 steps of 10 classes","model":"PPCA-SWSL","rank_in_archive_order":14,"of":15,"metrics":{"Final Accuracy":"77.07"},"uses_additional_data":true},{"leaderboard":"/sota/incremental-learning-on-cifar-100-50-classes-3","task":"Incremental Learning","dataset":"CIFAR-100 - 50 classes + 5 steps of 10 classes","model":"PPCA-CLIP","rank_in_archive_order":15,"of":15,"metrics":{"Final Accuracy":"69.71"},"uses_additional_data":true},{"leaderboard":"/sota/incremental-learning-on-imagenet-500-classes-2","task":"Incremental Learning","dataset":"ImageNet - 500 classes + 10 steps of 50 classes","model":"PPCA-CLIP","rank_in_archive_order":4,"of":4,"metrics":{"Final Accuracy":"71.25"},"uses_additional_data":true},{"leaderboard":"/sota/incremental-learning-on-imagenet-500-classes-1","task":"Incremental Learning","dataset":"ImageNet - 500 classes + 5 steps of 100 classes","model":"PPCA-CLIP","rank_in_archive_order":4,"of":4,"metrics":{"Final Accuracy":"71.25"},"uses_additional_data":true},{"leaderboard":"/sota/incremental-learning-on-imagenet-10k-5225","task":"Incremental Learning","dataset":"ImageNet-10k - 5225 classes + 5 steps of 1045 classes","model":"PPCA-CLIP","rank_in_archive_order":1,"of":1,"metrics":{"Final Accuracy":"35.42"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}