{"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/regularization-with-stochastic","title":"Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning","arxiv_id":"1606.04586","date":"2016-06-14","proceeding":"NeurIPS 2016 12","authors":["Mehdi Sajjadi","Mehran Javanmardi","Tolga Tasdizen"],"abstract":"Effective convolutional neural networks are trained on large sets of labeled\ndata. However, creating large labeled datasets is a very costly and\ntime-consuming task. Semi-supervised learning uses unlabeled data to train a\nmodel with higher accuracy when there is a limited set of labeled data\navailable. In this paper, we consider the problem of semi-supervised learning\nwith convolutional neural networks. Techniques such as randomized data\naugmentation, dropout and random max-pooling provide better generalization and\nstability for classifiers that are trained using gradient descent. Multiple\npasses of an individual sample through the network might lead to different\npredictions due to the non-deterministic behavior of these techniques. We\npropose an unsupervised loss function that takes advantage of the stochastic\nnature of these methods and minimizes the difference between the predictions of\nmultiple passes of a training sample through the network. We evaluate the\nproposed method on several benchmark datasets.","url_abs":"http://arxiv.org/abs/1606.04586v1","url_pdf":"http://arxiv.org/pdf/1606.04586v1.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":[],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"semi-supervised-image-classification","task_name":"Semi-Supervised Image Classification"}],"methods":[{"method_slug":"dropout","method_name":"Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn-1","task":"Semi-Supervised Image Classification","dataset":"SVHN, 250 Labels","model":"Ⅱ-model","rank_in_archive_order":14,"of":15,"metrics":{"Accuracy":"82.35"},"uses_additional_data":false},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-2","task":"Semi-Supervised Image Classification","dataset":"cifar-100, 10000 Labels","model":"Ⅱ-Model","rank_in_archive_order":29,"of":29,"metrics":{"Percentage error":"39.19"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.04586","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}