consistency regularization semi supervised learning

We extensively evaluate the proposed semi-supervised deep learning methods on two challenging medical image classification tasks: breast cancer screening from ultrasound images and multi-class ophthalmic disease classification from optical coherence tomography B-scan images. We identify that the main reason for this is the lack of consistency in . OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers [Link] KuniakiSaito1, DongyunKim1, Kate Saenko1,2 1: Boston University, 2: MIT-IBM Watson AI Lab This repository is for Adaptive Knowledge Consistency and Adaptive Representation Consistency introduced in the following paper: Abulikemu Abuduweili, Xingjian Li, Humphrey Shi, Cheng-Zhong Xu, and Dejing Dou, Adaptive Consistency Regularization for Semi-Supervised Transfer Learning, CVPR 2021. In this work, we consider semi-supervised learning and transfer learning jointly, "Temporal ensembling for semi-supervised learning." This repository is for Adaptive Knowledge Consistency and Adaptive Representation Consistency introduced in the following paper: Abulikemu Abuduweili, Xingjian Li, Humphrey Shi, Cheng-Zhong Xu, and Dejing Dou, Adaptive Consistency Regularization for Semi-Supervised Transfer Learning, CVPR 2021. sistency regularization technique for state-of-the-art semi-supervised learning. Sohn, Kihyuk, et al. These proceedings present selected research papers from CISC’18, held in Wenzhou, China. Found inside – Page 55walk [8, 168], harmonic function [210], local and global consistency [198], manifold regularization [17, 158, 155], kernels from the graph Laplacian [35, 51, 95, 101, 163,211], spectral graph transducer [90], local averaging [179, 187], ... 2.2 Semi-supervised classification. 2021 Jul;25(7):582-595. doi: 10.1016/j.tics.2021.03.016. Consistency regularization; Deep learning; Semi-supervised classification. More concretely, the model first uses its prediction for pseudo-labeling on the weakly-augmented input image. the target task. cross-entropy loss) with a consistency loss term. Scarcity of labeled data has motivated the development of semi-supervised learning methods, which learn from large portions of unlabeled data alongside a few labeled samples. However, in practice, unlabeled data can contain categories unseen in the labeled set, i.e., outliers, which can significantly harm the performance of SSL algorithms. Miyato, Takeru, et al. Found inside – Page 8We use semi-supervised learning to fit the ensemble models on the above two types of datasets with one-step loss and ... To improve consistency for ensemble models, we adopt the consistency regularization to constrain the structure ... We identify that the main reason for this is the lack of consistency in . The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, ... However, their performance still lags behind the state-of-the-art non-GAN based SSL approaches. Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on ... AKC and ARC regularization terms could be combined with other semi-supervised learning methods, Found inside – Page 59These methods train a classifier with an additional loss which induces consistent prediction by minimizing output ... Several recent studies on semi-supervised learning focused on regularization techniques which assure consistency or ... In this work, we proposed a novel semi-supervised deep learning method, i.e., deep virtual adversarial self-training with consistency regularization, for large-scale medical image classification. Time-Consistent Self-Supervision for Semi-Supervised Learning losses. "Interpolation consistency training for semi-supervised learning." Generative Adversarial Networks (GANs) based semi-supervised learning (SSL) approaches are shown to improve classification performance by utilizing a large number of unlabeled samples in conjunction with limited labeled samples. Consistency Regularization, Entropy Minimization, and Pseudo Labeling: The popular approaches to SSL add a new loss term during training to the typical Supervised Learning setting. Typical SSL methods like FixMatch assume that labeled and unlabeled data share the same label space. Epub 2018 Jul 23. In this work, we proposed a novel semi-supervised deep learning method, i.e., deep virtual adversarial self-training with consistency regularization, for large-scale medical . Typical SSL methods like FixMatch assume that labeled and unlabeled data share the same label space.

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