self training with noisy student improves imagenet classification

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. Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. We evaluate the best model, that achieves 87.4% top-1 accuracy, on three robustness test sets: ImageNet-A, ImageNet-C and ImageNet-P. ImageNet-C and P test sets[24] include images with common corruptions and perturbations such as blurring, fogging, rotation and scaling. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Self-training with Noisy Student improves ImageNet classification Finally, in the above, we say that the pseudo labels can be soft or hard. In contrast, the predictions of the model with Noisy Student remain quite stable. On . 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. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 Edit social preview. on ImageNet, which is 1.0 Do better imagenet models transfer better? Self-Training With Noisy Student Improves ImageNet Classification ImageNet-A top-1 accuracy from 16.6 Self-Training With Noisy Student Improves ImageNet Classification The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. As shown in Table3,4 and5, when compared with the previous state-of-the-art model ResNeXt-101 WSL[44, 48] trained on 3.5B weakly labeled images, Noisy Student yields substantial gains on robustness datasets. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. We apply dropout to the final classification layer with a dropout rate of 0.5. During this process, we kept increasing the size of the student model to improve the performance. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. self-mentoring outperforms data augmentation and self training. Noisy Student can still improve the accuracy to 1.6%. Especially unlabeled images are plentiful and can be collected with ease. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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. Self-training with Noisy Student improves ImageNet classification 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. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. It is experimentally validated that, for a target test resolution, using a lower train resolution offers better classification at test time, and a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ is proposed. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Self-training with Noisy Student improves ImageNet classification Abstract. Self-training with Noisy Student. Learn more. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. https://arxiv.org/abs/1911.04252. Self-Training With Noisy Student Improves ImageNet Classification We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This work proposes a novel architectural unit, which is term the Squeeze-and-Excitation (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and shows that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. The width. We improved it by adding noise to the student to learn beyond the teachers knowledge. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Use Git or checkout with SVN using the web URL. Noisy StudentImageNetEfficientNet-L2state-of-the-art. Similar to[71], we fix the shallow layers during finetuning. 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. On, International journal of molecular sciences. These CVPR 2020 papers are the Open Access versions, provided by the. Learn more. 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. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. . 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. 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. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. 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. 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. FixMatch-LS: Semi-supervised skin lesion classification with label Our work is based on self-training (e.g.,[59, 79, 56]). It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images 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. A semi-supervised segmentation network based on noisy student learning You signed in with another tab or window. 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. 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. 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. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. GitHub - google-research/noisystudent: Code for Noisy Student Training Their noise model is video specific and not relevant for image classification. Ranked #14 on However, in the case with 130M unlabeled images, with noise function removed, the performance is still improved to 84.3% from 84.0% when compared to the supervised baseline. The results also confirm that vision models can benefit from Noisy Student even without iterative training. For classes where we have too many images, we take the images with the highest confidence. . Self-training with Noisy Student - 3429-3440. . 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. Whether the model benefits from more unlabeled data depends on the capacity of the model since a small model can easily saturate, while a larger model can benefit from more data. 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. 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. We duplicate images in classes where there are not enough images. First, we run an EfficientNet-B0 trained on ImageNet[69]. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. sign in 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 model with Noisy Student can successfully predict the correct labels of these highly difficult 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 classication Qizhe Xie 1, Minh-Thang Luong , Eduard Hovy2, Quoc V. Le1 1Google Research, Brain Team, 2Carnegie Mellon University fqizhex, thangluong, qvlg@google.com, hovy@cmu.edu Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . We then use the teacher model to generate pseudo labels on unlabeled 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. In particular, we first perform normal training with a smaller resolution for 350 epochs. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. A tag already exists with the provided branch name. Noisy Student Explained | Papers With Code We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. We iterate this process by putting back the student as the teacher. Self-Training With Noisy Student Improves ImageNet Classification 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. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 2023.3.1_2 - We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. combination of labeled and pseudo labeled images. Self-training with Noisy Student improves ImageNet classification w Summary of key results compared to previous state-of-the-art models. 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]. 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. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . However an important requirement for Noisy Student to work well is that the student model needs to be sufficiently large to fit more data (labeled and pseudo labeled). to use Codespaces. 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 . Self-mentoring: : A new deep learning pipeline to train a self Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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. Noisy Student leads to significant improvements across all model sizes for EfficientNet. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. We iterate this process by 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. 10687-10698). Noisy Student Training is a semi-supervised learning approach. to use Codespaces. 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). 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. 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. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . . task. Noisy Student (EfficientNet) - huggingface.co 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. The performance drops when we further reduce it. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). 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. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. We then select images that have confidence of the label higher than 0.3. Works based on pseudo label[37, 31, 60, 1] are similar to self-training, but also suffers the same problem with consistency training, since it relies on a model being trained instead of a converged model with high accuracy to generate pseudo labels. 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. Self-training with Noisy Student improves ImageNet classification 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. team using this approach not only surpasses the top-1 ImageNet accuracy of SOTA models by 1%, it also shows that the robustness of a model also improves. Due to duplications, there are only 81M unique images among these 130M images. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. 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. Work fast with our official CLI. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. This model investigates a new method. ; 2006)[book reviews], Semi-supervised deep learning with memory, Proceedings of the European Conference on Computer Vision (ECCV), Xception: deep learning with depthwise separable convolutions, K. Clark, M. Luong, C. D. Manning, and Q. V. Le, Semi-supervised sequence modeling with cross-view training, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, AutoAugment: learning augmentation strategies from data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, RandAugment: practical data augmentation with no separate search, Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, Good semi-supervised learning that requires a bad gan, T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, A. Galloway, A. Golubeva, T. Tanay, M. Moussa, and G. W. Taylor, R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow, I. J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and harnessing adversarial examples, Semi-supervised learning by entropy minimization, Advances in neural information processing systems, K. Gu, B. Yang, J. Ngiam, Q. 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. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. [57] used self-training for domain adaptation. 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. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . 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. 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. This is probably because it is harder to overfit the large unlabeled dataset. We present a simple self-training method that achieves 87.4 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. 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. Self-Training : Noisy Student : EfficientNet with Noisy Student produces correct top-1 predictions (shown in. Self-Training With Noisy Student Improves ImageNet Classification Self-training with Noisy Student improves ImageNet classification. Summarization_self-training_with_noisy_student_improves_imagenet_classification. 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. We use EfficientNet-B4 as both the teacher and the student. We also list EfficientNet-B7 as a reference. Imaging, 39 (11) (2020), pp. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. Their purpose is different from ours: to adapt a teacher model on one domain to another. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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 mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. It is expensive and must be done with great care. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10687-10698, (2020 . Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. 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.

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