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. We used the version from [47], which filtered the validation set of ImageNet. IEEE Trans. These significant gains in robustness in ImageNet-C and ImageNet-P are surprising because our models were not deliberately optimizing for robustness (e.g., via data augmentation). In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. 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. We use EfficientNet-B4 as both the teacher and the student. The accuracy is improved by about 10% in most settings. For labeled images, we use a batch size of 2048 by default and reduce the batch size when we could not fit the model into the memory. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. In contrast, the predictions of the model with Noisy Student remain quite stable. We use EfficientNet-B0 as both the teacher model and the student model and compare using Noisy Student with soft pseudo labels and hard pseudo labels. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 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]. We use a resolution of 800x800 in this experiment. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Train a larger classifier on the combined set, adding noise (noisy student). To intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct. w Summary of key results compared to previous state-of-the-art models. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. [^reference-9] [^reference-10] A critical insight was to . Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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 also list EfficientNet-B7 as a reference. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. As can be seen from Table 8, the performance stays similar when we reduce the data to 116 of the total data, which amounts to 8.1M images after duplicating. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. Self-training In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from If nothing happens, download Xcode and try again. Iterative training is not used here for simplicity. combination of labeled 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. There was a problem preparing your codespace, please try again. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. https://arxiv.org/abs/1911.04252. We improved it by adding noise to the student to learn beyond the teachers knowledge. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet (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. Imaging, 39 (11) (2020), pp. 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. . Although noise may appear to be limited and uninteresting, when it is applied to unlabeled data, it has a compound benefit of enforcing local smoothness in the decision function on both labeled and unlabeled data. 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-A top-1 accuracy from 16.6 We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. Zoph et al. In this section, we study the importance of noise and the effect of several noise methods used in our model. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. 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. The baseline model achieves an accuracy of 83.2. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. By showing the models only labeled images, we limit ourselves from making use of unlabeled images available in much larger quantities to improve accuracy and robustness of state-of-the-art models. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Our main results are shown in Table1. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Finally, in the above, we say that the pseudo labels can be soft or hard. 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. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. 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. 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. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative The main use case of knowledge distillation is model compression by making the student model smaller. Soft pseudo labels lead to better performance for low confidence data. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. The main difference between Data Distillation and our method is that we use the noise to weaken the student, which is the opposite of their approach of strengthening the teacher by ensembling. Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y. We iterate this process by putting back the student as the teacher. to noise the student. On, International journal of molecular sciences. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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. on ImageNet, which is 1.0 This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. 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. 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. A tag already exists with the provided branch name. Learn more. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 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. We iterate this process by putting back the student as the teacher. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. 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. The results are shown in Figure 4 with the following observations: (1) Soft pseudo labels and hard pseudo labels can both lead to great improvements with in-domain unlabeled images i.e., high-confidence images. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. 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. 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. We will then show our results on ImageNet and compare them with state-of-the-art models. 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. Le. Copyright and all rights therein are retained by authors or by other copyright holders. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Agreement NNX16AC86A, Is ADS down? The proposed use of distillation to only handle easy instances allows for a more aggressive trade-off in the student size, thereby reducing the amortized cost of inference and achieving better accuracy than standard distillation. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. Models are available at this https URL. In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. 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. Self-Training Noisy Student " " Self-Training . 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 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. Med. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. It can be seen that masks are useful in improving classification performance. 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]. 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. 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. We also study the effects of using different amounts of unlabeled data. In other words, small changes in the input image can cause large changes to the predictions. 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. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Self-training with Noisy Student improves ImageNet classification. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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. possible. 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. 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. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and 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. all 12, Image Classification We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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. (or is it just me), Smithsonian Privacy Do better imagenet models transfer better? Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Please refer to [24] for details about mCE and AlexNets error rate. . We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. For more information about the large architectures, please refer to Table7 in Appendix A.1. 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. Noisy StudentImageNetEfficientNet-L2state-of-the-art. Learn more. Further, Noisy Student outperforms the state-of-the-art accuracy of 86.4% by FixRes ResNeXt-101 WSL[44, 71] that requires 3.5 Billion Instagram images labeled with tags. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. 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. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. We iterate this process by Note that these adversarial robustness results are not directly comparable to prior works since we use a large input resolution of 800x800 and adversarial vulnerability can scale with the input dimension[17, 20, 19, 61]. Code for Noisy Student Training. 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. If nothing happens, download GitHub Desktop and try again. 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. A number of studies, e.g. Self-Training With Noisy Student Improves ImageNet Classification. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 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 purpose is different from ours: to adapt a teacher model on one domain to another. During the generation of the pseudo Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. We duplicate images in classes where there are not enough images. We then select images that have confidence of the label higher than 0.3. For example, without Noisy Student, the model predicts bullfrog for the image shown on the left of the second row, which might be resulted from the black lotus leaf on the water. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. Edit social preview. A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet. For each class, we select at most 130K images that have the highest confidence.

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