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Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Noisy Student (B7, L2) means to use EfficientNet-B7 as the student and use our best model with 87.4% accuracy as the teacher model. For example, with all noise removed, the accuracy drops from 84.9% to 84.3% in the case with 130M unlabeled images and drops from 83.9% to 83.2% in the case with 1.3M unlabeled images. to use Codespaces. We used the version from [47], which filtered the validation set of ImageNet. Self-training was previously used to improve ResNet-50 from 76.4% to 81.2% top-1 accuracy[76] which is still far from the state-of-the-art accuracy. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative We also study the effects of using different amounts of unlabeled data. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Self-training with Noisy Student improves ImageNet classification. We iterate this process by putting back the student as the teacher. We then perform data filtering and balancing on this corpus. In particular, we set the survival probability in stochastic depth to 0.8 for the final layer and follow the linear decay rule for other layers. In terms of methodology, In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. If nothing happens, download Xcode and try again. This result is also a new state-of-the-art and 1% better than the previous best method that used an order of magnitude more weakly labeled data [ 44, 71]. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. Ranked #14 on 10687-10698 Abstract On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. Significantly, after using the masks generated by student-SN, the classification performance improved by 0.9 of AC, 0.7 of SE, and 0.9 of AUC. We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. A tag already exists with the provided branch name. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. Our experiments showed that self-training with Noisy Student and EfficientNet can achieve an accuracy of 87.4% which is 1.9% higher than without Noisy Student. The performance drops when we further reduce it. We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. Flip probability is the probability that the model changes top-1 prediction for different perturbations. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Self-training with Noisy Student improves ImageNet classificationCVPR2020, Codehttps://github.com/google-research/noisystudent, Self-training, 1, 2Self-training, Self-trainingGoogleNoisy Student, Noisy Studentstudent modeldropout, stochastic depth andaugmentationteacher modelNoisy Noisy Student, Noisy Student, 1, JFT3ImageNetEfficientNet-B00.3130K130K, EfficientNetbaseline modelsEfficientNetresnet, EfficientNet-B7EfficientNet-L0L1L2, batchsize = 2048 51210242048EfficientNet-B4EfficientNet-L0l1L2350epoch700epoch, 2EfficientNet-B7EfficientNet-L0, 3EfficientNet-L0EfficientNet-L1L0, 4EfficientNet-L1EfficientNet-L2, student modelNoisy, noisystudent modelteacher modelNoisy, Noisy, Self-trainingaugmentationdropoutstochastic depth, Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores., 12/self-training-with-noisy-student-f33640edbab2, EfficientNet-L0EfficientNet-B7B7, EfficientNet-L1EfficientNet-L0, EfficientNetsEfficientNet-L1EfficientNet-L2EfficientNet-L2EfficientNet-B75. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. We use the same architecture for the teacher and the student and do not perform iterative training. We do not tune these hyperparameters extensively since our method is highly robust to them. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Code for Noisy Student Training. We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . Noise Self-training with Noisy Student 1. Classification of Socio-Political Event Data, SLADE: A Self-Training Framework For Distance Metric Learning, Self-Training with Differentiable Teacher, https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. The architectures for the student and teacher models can be the same or different. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. over the JFT dataset to predict a label for each image. Specifically, as all classes in ImageNet have a similar number of labeled images, we also need to balance the number of unlabeled images for each class. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . The learning rate starts at 0.128 for labeled batch size 2048 and decays by 0.97 every 2.4 epochs if trained for 350 epochs or every 4.8 epochs if trained for 700 epochs. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Since we use soft pseudo labels generated from the teacher model, when the student is trained to be exactly the same as the teacher model, the cross entropy loss on unlabeled data would be zero and the training signal would vanish. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. This result is also a new state-of-the-art and 1% better than the previous best method that used an order of magnitude more weakly labeled data[44, 71]. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. ImageNet . Parthasarathi et al. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single model. We start with the 130M unlabeled images and gradually reduce the number of images. We then select images that have confidence of the label higher than 0.3. The main difference between our work and prior works is that we identify the importance of noise, and aggressively inject noise to make the student better. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. This material is presented to ensure timely dissemination of scholarly and technical work. If nothing happens, download GitHub Desktop and try again. These CVPR 2020 papers are the Open Access versions, provided by the. Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. Use, Smithsonian Train a larger classifier on the combined set, adding noise (noisy student). Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. Infer labels on a much larger unlabeled dataset. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. As noise injection methods are not used in the student model, and the student model was also small, it is more difficult to make the student better than teacher. International Conference on Machine Learning, Learning extraction patterns for subjective expressions, Proceedings of the 2003 conference on Empirical methods in natural language processing, A. Roy Chowdhury, P. Chakrabarty, A. Singh, S. Jin, H. Jiang, L. Cao, and E. G. Learned-Miller, Automatic adaptation of object detectors to new domains using self-training, T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, Probability of error of some adaptive pattern-recognition machines, W. Shi, Y. Gong, C. Ding, Z. MaXiaoyu Tao, and N. Zheng, Transductive semi-supervised deep learning using min-max features, C. Simon-Gabriel, Y. Ollivier, L. Bottou, B. Schlkopf, and D. Lopez-Paz, First-order adversarial vulnerability of neural networks and input dimension, Very deep convolutional networks for large-scale image recognition, N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. Noisy Student Training is a semi-supervised learning method which achieves 88.4% top-1 accuracy on ImageNet (SOTA) and surprising gains on robustness and adversarial benchmarks. Not only our method improves standard ImageNet accuracy, it also improves classification robustness on much harder test sets by large margins: ImageNet-A[25] top-1 accuracy from 16.6% to 74.2%, ImageNet-C[24] mean corruption error (mCE) from 45.7 to 31.2 and ImageNet-P[24] mean flip rate (mFR) from 27.8 to 16.1. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: For ImageNet checkpoints trained by Noisy Student Training, please refer to the EfficientNet github. A common workaround is to use entropy minimization or ramp up the consistency loss. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. After using the masks generated by teacher-SN, the classification performance improved by 0.2 of AC, 1.2 of SP, and 0.7 of AUC. It is expensive and must be done with great care. Self-training with Noisy Student improves ImageNet classification. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. The paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task is revisited, and a simple recipe that is called Big Transfer (BiT) is created, which achieves strong performance on over 20 datasets. Code is available at https://github.com/google-research/noisystudent. Med. Do imagenet classifiers generalize to imagenet? We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled images. Self-training with Noisy Student. Please We duplicate images in classes where there are not enough images. To date (2020) we will introduce "Noisy Student Training", which is a state-of-the-art model.The idea is to extend self-training and Distillation, a paper that shows that by adding three noises and distilling multiple times, the student model will have better generalization performance than the teacher model. We then use the teacher model to generate pseudo labels on unlabeled images. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. Self-Training With Noisy Student Improves ImageNet Classification Abstract: We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. The accuracy is improved by about 10% in most settings. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Self-training is a form of semi-supervised learning [10] which attempts to leverage unlabeled data to improve classification performance in the limited data regime. https://arxiv.org/abs/1911.04252, Accompanying notebook and sources to "A Guide to Pseudolabelling: How to get a Kaggle medal with only one model" (Dec. 2020 PyData Boston-Cambridge Keynote), Deep learning has shown remarkable successes in image recognition in recent years[35, 66, 62, 23, 69]. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. To achieve this result, we first train an EfficientNet model on labeled ImageNet images and use it as a teacher to generate pseudo labels on 300M unlabeled images. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Noisy Students performance improves with more unlabeled data. Copyright and all rights therein are retained by authors or by other copyright holders. Finally, frameworks in semi-supervised learning also include graph-based methods [84, 73, 77, 33], methods that make use of latent variables as target variables [32, 42, 78] and methods based on low-density separation[21, 58, 15], which might provide complementary benefits to our method. We will then show our results on ImageNet and compare them with state-of-the-art models. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher. It can be seen that masks are useful in improving classification performance. We then train a larger EfficientNet as a student model on the You signed in with another tab or window. Notice, Smithsonian Terms of et al. Self-training with noisy student improves imagenet classification. At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Then, that teacher is used to label the unlabeled data. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Use Git or checkout with SVN using the web URL. ImageNet-A top-1 accuracy from 16.6 Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. 10687-10698). IEEE Trans. We apply dropout to the final classification layer with a dropout rate of 0.5. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images But during the learning of the student, we inject noise such as data Our procedure went as follows. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. Noisy Student Training extends the idea of self-training and distillation with the use of equal-or-larger student models and noise added to the student during learning. See To achieve strong results on ImageNet, the student model also needs to be large, typically larger than common vision models, so that it can leverage a large number of unlabeled images. We iterate this process by augmentation, dropout, stochastic depth to the student so that the noised Zoph et al. Our main results are shown in Table1. . In particular, we first perform normal training with a smaller resolution for 350 epochs. Add a This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. However, during the learning of the student, we inject noise such as dropout, stochastic depth and data augmentation via RandAugment to the student so that the student generalizes better than the teacher. Here we show the evidence in Table 6, noise such as stochastic depth, dropout and data augmentation plays an important role in enabling the student model to perform better than the teacher. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from