Found inside – Page 358... dispersed when adaptation is achieved and the proposed method performs better than TRIPPLE. 5.3 Semi-supervised IDDA We evaluate our method for MNIST → SVHN, which is compared to Mean Teacher for semi-supervised learning over SVHN. 1.1.2 Semi-Supervised Learning Semi-supervised learning (SSL) is halfway between supervised and unsupervised learning. First two authors contributed equally to this work. ) Training deep neural networks usually requires a large amount of labeled data to obtain good performance. In this paper, we present a novel uncertainty-aware semi-supervised framework for left atrium segmentation from 3D MR images. 2.3 Mean teachers (NIPS 2017) 论文标题: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Subsequently we will present our modi cations that enable domain adaptation. These two frameworks are discussed in more detail in sections 2.2.1 and 2.2.2. . We demonstrate how NoTeacher can be customized to handle a range . The proposed learning scheme explicitly connects the learning across labeled and unlabeled clients by aligning their extracted disease relationships, thereby mitigating the deficiency of task knowledge at unlabeled clients and promoting discriminative information from unlabeled samples. Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification. << /Type /XRef /Length 75 /Filter /FlateDecode /DecodeParms << /Columns 5 /Predictor 12 >> /W [ 1 3 1 ] /Index [ 14 56 ] /Info 12 0 R /Root 16 0 R /Size 70 /Prev 140727 /ID [<6550bf384003308ca6f8408b8f99e15b>] >> Specifically, to improve the quality of pseudo la-bels while ensuring the model convergence during semi-supervised training, we store historical pseudo labels in memory and use nonmaximum suppression (NMS) to fuse the up-to-date detection result with the historical pseudo la-bels. 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, ... by each author's copyright. %���� Found inside – Page 305The second, Mean Teacher [42] is a semi-supervised method that creates two supervised neural networks, a teacher network and a student network, and trains both networks using randomly perturbed data. Training enforces a consistency loss ... Authors: Fengbei Liu, Yu Tian, Filipe R. Cordeiro, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro. "Semi-supervised" (SSL) ImageNet models are pre-trained on a subset of unlabeled YFCC100M public image dataset and fine-tuned with the ImageNet1K training dataset, as described by the semi-supervised training framework in the paper mentioned above. Recently, a number of semi-supervised learning (SSL) methods, in the framework of deep learning (DL), were shown to achieve state-of-the-art results on image datasets, while using a (very) limited amount of labeled data. 0.952±0.027; 0.916±0.022 vs. 0.918±0.017). Here for the first time, we propose a new method for the automatic segmentation of multiple tissue structures for knee arthroscopy. Semi supervised learning (SSL) has been found to be very useful in computer vision and natural language processing. Found inside – Page 326arXiv preprint arXiv:1610.02242 (2016) Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing ... with mean teachers for semi-supervised object detection (ISMT). Index Terms— Audio tagging, semi-supervised learning, deep co-training, mean-teacher 1. It consists of the following steps: Take a supervised architecture and make a copy of it. CRAL predicts the presence of multiple pathologies in a class-specific attentive view. The proposed grasping detection network specially provides a prediction uncertainty estimation mechanism by leveraging on Feature Pyramid Network (FPN), and the mean-teacher semi-supervised learning utilizes such uncertainty information to emphasizing the consistency loss only for those unlabelled data with high confidence, which we referred it . Found inside – Page 152Semi-supervised brain lesion segmentation with an adapted mean teacher model. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 554–565. Springer, Cham (2019). https://doi.org/10. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. Semi-supervised deep learning has attracted increas-ing attention in recent years (Sajjadi, Javanmardi, and Tas- . NerulPS 2018; Tarvainen and Valpora. Specifically, the proposed framework consists of two modules: feature embedding module and attention learning module. @InProceedings{Wang_2021_ICCV, Found inside – Page 2633.5 Comparison to Previous Work We also compare our method with currently popular methods: mean teacher(MT) [13], deep adversarial ... When M = 2, detection result of semi-supervised learning even exceeds that of supervised learning. The structure of the mean teacher model [29] { also discussed in section 2.1 { is shown in Figure 2a. 16 0 obj similar to the Mean Teacher, but there is a critical differ-ence that significantly improves our performance. Download PDF. This book constitutes the refereed proceedings of the 14th International Symposium on Perception, Representations, Image, Sound, Music, CMMR 2019, held in Marseille, France, in October 2019. data. [16] designed an uncertainty-aware mean teacher framework, For every example, this mean teacher model is then used to obtain proxy labels \(\tilde{z}\). Found inside – Page 550Although semi-supervised learning with pseudo annotations has shown promising performance, model-generated annotations can still be noisy and has ... [27] extended the mean teacher paradigm [21] with the guidance of uncertainty ... Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. �lf�(Kゕ�L3��*�;�;���y�6K�}��u���K � | In addition to unlabeled data, the algorithm is provided with some super- Mean teachers are better role models: Weighted-averaged consistency targets improve semi-supervised deep learning results. Our proposed approaches resulted in average AUCs up to 0.6691 with only 25 labeled samples per class, and an average AUC of 0.7182 when using only 2% of the labeled data, achieving results superior to previous approaches on semi-supervised chest radiograph classification. The Mean Teacher (MT) model of Tarvainen and Valpola has shown favorable performance on several semi-supervised benchmark datasets. Training deep convolutional neural networks usually requires a large amount of labeled data. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen The Curious AI Company . To make use of unlabeled data, current popular semi-supervised methods (e.g., temporal ensembling, mean teacher) mainly impose data-level and model-level consistency on unlabeled data. Comparison of Deep Co-Training and Mean-Teacher Approaches for Semi-Supervised Audio Tagging Abstract: Recently, a number of semi-supervised learning (SSL) methods, in the framework of deep learning (DL), were shown to achieve state-of-the-art results on image datasets, while using a (very) limited amount of labeled data. [�X��W�~zvy��o�������������e�&K�W�*�a^8}�vyq�,�c����3���������K�YP6S�� �S��M��T�7].����mU���`�u�� ���j�f�����"�Á�ۂOx_�>�|��p��O4�3Y�LطȔ�wm��3���!h�4�@xr�z�:��u������y��#,�~��3�)=Y�JGN�~�9F�P��{YC��b�8��. Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification 7 Label Percentage 2% 5% 10% 15% 20% 100% Graph XNet [1] 53.00 58.00 63.00 68.00 78.00 N/A diverse set of real-world regression tasks over supervised deep kernel learning and semi-supervised methods such as VAT and mean teacher adapted for regression. A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection. When a percept is changed slightly, a human typically still considers it to be the same object. Also, ideas of self-supervised learning have been exploited to tackle the semi-supervised learning recently [54,5], and we also in-corporate the contrastive loss that has been well studied in the self-supervised learning [17,19,11,27,9,10] as the When training with unlabeled data, the teacher network is employed to predict pseudo labels for student network training, which allows it to learn from unlabeled data. Paper---- NIPS 2017 poster---- NIPS 2017 spotlight slides---- Blog post. the ChestX-ray14 dataset. Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning Zhanghan Ke1,2 ∗ Daoye Wang2 Qiong Yan2 Jimmy Ren2 Rynson W.H. To the best of our knowledge, this is the first method that exploits graph-based semi-supervised learning for X-ray data classification. The Mean Teacher (MT) model of Tarvainen and Valpola has shown favorable performance on several semi-supervised benchmark datasets. Further, the syntax-aware model out- . Found inside – Page 666Test results on LC07-15 dataset ( Ladder Network [ 8 ] , II - Model ( 10 ) , Mean Teacher [ 11 ] , MixMatch [ 12 ] and ... We showed how meanNet used multi - layer label mean to improve the performance of semi - supervised learning . This approach largely prevents the student model to learn the incorrect/harmful information from the consistency loss . We adapted methods based on pseudo-labeling and consistency regularization to perform multi-label classification and to use a state-of-the-art model architecture in chest radiograph classification. << /Filter /FlateDecode /S 64 /Length 86 >> By Antti Tarvainen, Harri Valpola (The Curious AI Company) Approach. The feature embedding module learns high-level features with a convolutional neural network (CNN) while the attention learning module focuses on exploring the assignment scheme of different categories. NIPS 2017 In this paper, we address the above problem by proposing a category-wise residual attention learning (CRAL) framework. We hypothesize that semi-supervised deep learning leveraging a small amount of labeled data with abundant available unlabeled data can provide a powerful alternative strategy. We adapt the mean teacher model, which is originally developed for SSL-based image classification, for brain lesion segmentation. updating teacher via EMA with a sufficiently large discount factor. title = {A Multi-Task Mean Teacher for Semi-Supervised Facial Affective Behavior Analysis}, endobj Tofurtherimproveperformance,wealsointroducesemi-supervised learning based on mean teacher [14], data augmentation techniques such as time-shifting [8] and mixup [15], event-dependent post-processing refinement, and posterior-level score fusion. Found inside – Page 535... so semi-supervised learning methods can be applied to enhance the robustness of the model and raise model prediction accuracy. The main method used in this paper is the mean teacher method, which contains a student network and a ... Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification 3 Fig.1: Description of the proposed self-supervised mean-teacher for semi-supervised (S 2 MTS ) learning. effectively to reduce over-fitting in semi-supervised learning. In this post, I will illustrate the key ideas of these recent methods for . Mean teacher has achieved state-of-the-art results for semi-supervised learning for computer vision. While the reported segmentation method is of great applicability in terms of contextual awareness for the surgical team, it can also be used for medical robotic applications such as SLAM and depth mapping. semi-supervised learning. 19 0 obj The training data of 3868 images were collected from 4 cadaver experiments, 5 knees, and manually contoured by two clinicians into four classes: Femur, Anterior Cruciate Ligament (ACL), Tibia, and Meniscus. However, MT is known to suffer from confirmation . Found inside – Page 251(2) Learning to Rank (L2R): a semi-supervised crowd counting method proposed in [33]. As the unlabeled images used in this ... (4) Mean teacher (MT): Mean teacher [40] is a classic consistency-based semisupervised learning approach. Considering that human diagnosis often refers to previous analogous cases to make reliable decisions, we introduce a novel sample relation consistency (SRC) paradigm to effectively exploit unlabeled data by modeling the relationship information among different samples. We con-ducted experimental evaluations using the DCASE2020 Task4 val- To further regularise the depth estimation, we propose the use of clean training images captured by the stereo arthroscope of routine objects (presenting none of the poor imaging conditions and with rich texture information) to pre-train the model. � The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019. Our method outperforms the state-of-the-art semi-supervised methods, demonstrating the potential of our framework for the challenging semi-supervised problems. In this case, the high capacity teacher model was trained only with labeled examples. Yu et al. Identifying one or more pathologies from a chest X-ray image is often hindered by the pathologies unrelated to the targets. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Virtual adversarial training: a regularization method for supervised and semi-supervised learning. 17 0 obj "Semi-supervised" (SSL) ImageNet models are pre-trained on a subset of unlabeled YFCC100M public image dataset and fine-tuned with the ImageNet1K training dataset, as described by the semi-supervised training framework in the paper mentioned above. named \textit{Federated Semi-supervised Learning} (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i.e., hospitals). show that the SSL Mean Teacher approach nears the performance of fully-supervised ap-proaches even with only 10% of the labeled corpus. endobj Paper---- NIPS 2017 poster---- NIPS 2017 spotlight slides---- Blog post. Semi-supervised classification of radiology images with NoTeacher: A teacher that is not mean. Mean teachers are better role models. Our framework can effectively leverage the unlabeled data by encouraging consistent predictions of the same input under different perturbations. Found inside – Page 13Semi-supervised. Segmentation. Using. Mean. Teacher. Given that the classification cost for unlabeled samples is undefined in supervised learning, adding unlabeled samples into the training procedure can be quite challenging. In this post, I will illustrate the key ideas of these recent methods for . Mean teachers are better role models:Weight-averaged consistency targets improve semi-supervised deep learning results. In this post, I will be discussing and implementing "MixMatch: A Holistic Approach to Semi-Supervised Learning;" by Berthelot, Carlini, Goodfellow, Oliver, Papernot and Raffel [1]. However, obtaining high-quality annotations is a laboursome and expensive process due to the need of expert radiologists for the . Our approach adapts the U-net and the U-net++ architectures for this segmentation task. After that, the proposed model is assigned to be student and teacher networks. Tofurtherimproveperformance,wealsointroducesemi-supervised learning based on mean teacher [14], data augmentation techniques such as time-shifting [8] and mixup [15], event-dependent post-processing refinement, and posterior-level score fusion. NIPS 2017. 4. with mean teachers for semi-supervised object detection (ISMT). Instead of feeding two strongly augmented copies to the teacher Meanwhile, the relevant features would be strengthened by assigning larger weights. & Valpola, H. Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. The student network is trained using gradient descent, Furthermore, a confidence-driven mean teacher semi-supervised learning is used to adapt the domain by leveraging the cheap but massive unlabelled data with only a few labelled data. We demonstrate, through a set of numerical and visual experiments, that our method produces highly competitive results on the ChestX-ray14 data set whilst drastically reducing the need for annotated data. We validate S\(^2\)MTS\(^2\) on the multi-label classification problems from Chest X-ray14 and CheXpert, and the multi-class classification from ISIC2018, where we show that it outperforms the previous SOTA semi-supervised learning methods by a large margin. To be specific, a multi-task model is proposed to learn three different kinds of facial affective representations simultaneously. Found insideUnlike supervised machine learning, the unsupervised version misses feedback from the absent teacher, thus the learners must learn themselves. The term 'ab-sent teacher' means that the available training samples xi lack their ... data without expert-annotated ground truth. Found inside – Page 1902.3 Mean Teachers In the region of semi-supervised learning technology, self-training method is one of the basic useful methods. For unannotated data, the predictions from pre-trained model with high confidence will be treated as real ... Semi-Supervised Teacher-Student Architecture for Relation Extraction Fan Luo, Ajay Nagesh, Rebecca Sharp, and Mihai Surdeanu . The effectiveness of our method has been demonstrated with the clear improvements over state-of-the-arts as well as the thorough ablation analysis on both tasks\footnote{Code will be made available at \url{https://github.com/liuquande/FedIRM}}. x�cbd`�g`b``8 "����� �q ��"��Hi)�"�JA$W���"m�IF?m&��/6��20�4 g Specifically, to improve the quality of pseudo la-bels while ensuring the model convergence during semi-supervised training, we store historical pseudo labels in memory and use nonmaximum suppression (NMS) to fuse the up-to-date detection result with the historical pseudo la-bels. approaches to semi-supervised learning were inspired by the success of self-ensembling method. Comparison of Deep Co-Training and Mean-Teacher Approaches for Semi-Supervised Audio Tagging Abstract: Recently, a number of semi-supervised learning (SSL) methods, in the framework of deep learning (DL), were shown to achieve state-of-the-art results on image datasets, while using a (very) limited amount of labeled data. Local and global consistency regularized mean teacher for semi-supervised nuclei classification H Su, X Shi, J Cai, L Yang International Conference on Medical Image Computing and Computer-Assisted … , 2019 The teacher model parameter is updated with exponen-. Found inside – Page 336This training scheme was a remnant from the mean teacher semi-supervised training [28]. We did not benchmark its real potential but expect it to produce a more generalizable model, to prevent from overfitting on the training set and to ... 03/05/2021 ∙ by Fengbei Liu, et al. By Antti Tarvainen, Harri Valpola (The Curious AI Company) Approach. stream Using our clean data performance estimation, we notice that the majority of label noise on Chest X-ray14 is present in the class 'No Finding', which is intuitively correct because this is the most likely class to contain one or more of the 14 diseases due to labelling mistakes. Found inside – Page 127We improve this by adopting ideas from semi-supervised learning and introducing them to ACE, as described next. 2.3 Learning from Non-annotated Regions via Mean Teacher We here interpret samples that are assigned the background class in ... In this case, the high capacity teacher model was trained only with labeled examples. year = {2021}, Whilst supervised deep learning methods rely upon huge amounts of labelled data, the critical problem of achieving a good classification accuracy when an extremely small amount of labelled data is available has yet to be tackled. Temporal Ensembling for Semi-Supervised Learning. endstream Mean-teacher-Semi-supervised-semantic-segmnetation-This folder contains code for semi supervised semantic segmentation booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, Mean Teacher is a simple method for semi-supervised learning. On the other hand, semi-supervised learning (SSL) is a cost-efficient solution to combat lack of training data. 3.1 Why semi-supervised semantic segmentation is challenging. 14 0 obj INTRODUCTION Semi-supervised learning (SSL) approaches utilize a set of labeled data and a larger set of unlabeled data that are cheaper and faster to obtain. [5] embedded the transformation consistency into -model [3] to enhance the regularization for pixel-wise pre-dictions. However, it is expensive and time-consuming to annotate data for medical image segmentation tasks. Transformer (Ours): The proposed Transformer-based model. Humble Teachers Teach Better Students for Semi-Supervised Object Detection Yihe Tang†,* Weifeng Chen . In this paper, we present a novel relation-driven semi-supervised framework for medical image classification. However, due to poor imaging conditions (e.g., low texture, overexposure, etc. Corre- FLINS, an acronym introduced in 1994 and originally for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended into a well-established international research forum to advance the foundations and applications of ... Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification. The teacher model parameter is, . We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information. The dropout rate was set to 0.1. This paper considers the problem of multi-label thorax disease classification on chest X-ray images. Advances in Neural Information Processing Systems . . These ICCV 2021 workshop papers are the Open Access versions, provided by the. Using a data set containing 3868 arthroscopic images captured during cadaveric knee arthroscopy with semantic segmentation annotations, 2000 stereo image pairs of cadaveric knee arthroscopy, and 2150 stereo image pairs of routine objects, we show that our semantic segmentation regularised by self-supervised depth estimation produces a more accurate segmentation than a state-of-the-art semantic segmentation approach modeled exclusively with semantic segmentation annotation. Semi-supervised learning methods for Computer Vision have been advancing quickly in the past few years. Superior to existing consistency-based methods which simply enforce consistency of individual predictions, our framework explicitly enforces the consistency of semantic relation among different samples under perturbations, encouraging the model to explore extra semantic information from unlabeled data.
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