As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. 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. We iterate this process by putting back the student as the teacher. Specifically, we train the student model for 350 epochs for models larger than EfficientNet-B4, including EfficientNet-L0, L1 and L2 and train the student model for 700 epochs for smaller models. 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. Finally, we iterate the algorithm a few times by treating the student as a teacher to generate new pseudo labels and train a new student. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. For each class, we select at most 130K images that have the highest confidence. In other words, the student is forced to mimic a more powerful ensemble model. to use Codespaces. Iterative training is not used here for simplicity. Similar to[71], we fix the shallow layers during finetuning. 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. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Learn more. We use EfficientNet-B4 as both the teacher and the student. Do imagenet classifiers generalize to imagenet? The mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. Train a larger classifier on the combined set, adding noise (noisy student). The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. 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]. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. This work introduces two challenging datasets that reliably cause machine learning model performance to substantially degrade and curates an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out- of-dist distribution detection dataset created for ImageNet models. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Edit social preview. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. If nothing happens, download Xcode and try again. The performance consistently drops with noise function removed. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. We sample 1.3M images in confidence intervals. But during the learning of the student, we inject noise such as data On, International journal of molecular sciences. 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. For RandAugment, we apply two random operations with the magnitude set to 27. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. 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. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. (using extra training data). Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. . Train a larger classifier on the combined set, adding noise (noisy student). Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Unlike previous studies in semi-supervised learning that use in-domain unlabeled data (e.g, ., CIFAR-10 images as unlabeled data for a small CIFAR-10 training set), to improve ImageNet, we must use out-of-domain unlabeled data. 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 comparison is shown in Table 9. 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. Their main goal is to find a small and fast model for deployment. See Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . It can be seen that masks are useful in improving classification performance. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. We first report the validation set accuracy on the ImageNet 2012 ILSVRC challenge prediction task as commonly done in literature[35, 66, 23, 69] (see also [55]). Self-Training With Noisy Student Improves ImageNet Classification @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019 . We determine number of training steps and the learning rate schedule by the batch size for labeled images. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. For simplicity, we experiment with using 1128,164,132,116,14 of the whole data by uniformly sampling images from the the unlabeled set though taking the images with highest confidence leads to better results. When data augmentation noise is used, the student must ensure that a translated image, for example, should have the same category with a non-translated image. 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. Noise Self-training with Noisy Student 1. The most interesting image is shown on the right of the first row. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. (Submitted on 11 Nov 2019) We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. The best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. Since a teacher models confidence on an image can be a good indicator of whether it is an out-of-domain image, we consider the high-confidence images as in-domain images and the low-confidence images as out-of-domain images. ImageNet . These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). [^reference-9] [^reference-10] A critical insight was to . Proceedings of the eleventh annual conference on Computational learning theory, Proceedings of the IEEE conference on computer vision and pattern recognition, Empirical Methods in Natural Language Processing (EMNLP), Imagenet classification with deep convolutional neural networks, Domain adaptive transfer learning with specialist models, Thirty-Second AAAI Conference on Artificial Intelligence, Regularized evolution for image classifier architecture search, Inception-v4, inception-resnet and the impact of residual connections on learning. You signed in with another tab or window. Code is available at https://github.com/google-research/noisystudent. Here we use unlabeled images to improve the state-of-the-art ImageNet accuracy and show that the accuracy gain has an outsized impact on robustness. In the following, we will first describe experiment details to achieve our results. During this process, we kept increasing the size of the student model to improve the performance. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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. 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. ImageNet-A top-1 accuracy from 16.6 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. Our study shows that using unlabeled data improves accuracy and general robustness. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative 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. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical 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. 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. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. We then perform data filtering and balancing on this corpus. Please Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. These CVPR 2020 papers are the Open Access versions, provided by the. Chowdhury et al. Use Git or checkout with SVN using the web URL. If nothing happens, download GitHub Desktop and try again. We use a resolution of 800x800 in this experiment. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. 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. We iterate this process by putting back the student as the teacher. Here we study how to effectively use out-of-domain data. Please refer to [24] for details about mCE and AlexNets error rate. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. The performance drops when we further reduce it. 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. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Use Git or checkout with SVN using the web URL. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. Computer Science - Computer Vision and Pattern Recognition. 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. Our procedure went as follows. Here we study if it is possible to improve performance on small models by using a larger teacher model, since small models are useful when there are constraints for model size and latency in real-world applications. Use, Smithsonian In other words, small changes in the input image can cause large changes to the predictions. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. On robustness test sets, it improves The baseline model achieves an accuracy of 83.2. Self-training with Noisy Student improves ImageNet classification Original paper: https://arxiv.org/pdf/1911.04252.pdf Authors: Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le HOYA012 Introduction EfficientNet ImageNet SOTA EfficientNet The abundance of data on the internet is vast. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen, GPipe: efficient training of giant neural networks using pipeline parallelism, A. Iscen, G. Tolias, Y. Avrithis, and O. 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. We then train a larger EfficientNet as a student model on the Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. In this section, we study the importance of noise and the effect of several noise methods used in our model. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. 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. 10687-10698 Abstract This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. CLIP (Contrastive Language-Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.The idea of zero-data learning dates back over a decade [^reference-8] but until recently was mostly studied in computer vision as a way of generalizing to unseen object categories. putting back the student as the teacher. The results also confirm that vision models can benefit from Noisy Student even without iterative training. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks.

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