self training with noisy student improves imagenet classification

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]. A common workaround is to use entropy minimization or ramp up the consistency loss. Self-training with Noisy Student improves ImageNet classification. Self-training with Noisy Student - Medium One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. Self-training first uses labeled data to train a good teacher model, then use the teacher model to label unlabeled data and finally use the labeled data and unlabeled data to jointly train a student model. Do better imagenet models transfer better? Self-training with Noisy Student improves ImageNet classification corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. We then train a larger EfficientNet as a student model on the FixMatch-LS: Semi-supervised skin lesion classification with label Self-Training With Noisy Student Improves ImageNet Classification Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence. 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. combination of labeled and pseudo labeled images. 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. Next, a larger student model is trained on the combination of all data and achieves better performance than the teacher by itself.OUTLINE:0:00 - Intro \u0026 Overview1:05 - Semi-Supervised \u0026 Transfer Learning5:45 - Self-Training \u0026 Knowledge Distillation10:00 - Noisy Student Algorithm Overview20:20 - Noise Methods22:30 - Dataset Balancing25:20 - Results30:15 - Perturbation Robustness34:35 - Ablation Studies39:30 - Conclusion \u0026 CommentsPaper: https://arxiv.org/abs/1911.04252Code: https://github.com/google-research/noisystudentModels: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnetAbstract:We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. However, manually annotating organs from CT scans is time . We iterate this process by putting back the student as the teacher. We iterate this process by putting back the student as the teacher. We apply dropout to the final classification layer with a dropout rate of 0.5. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. 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. The baseline model achieves an accuracy of 83.2. Due to duplications, there are only 81M unique images among these 130M images. Train a larger classifier on the combined set, adding noise (noisy student). Especially unlabeled images are plentiful and can be collected with ease. Notice, Smithsonian Terms of Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. . Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Image Classification Self-Training With Noisy Student Improves ImageNet Classification Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). 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. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. These CVPR 2020 papers are the Open Access versions, provided by the. Compared to consistency training[45, 5, 74], the self-training / teacher-student framework is better suited for ImageNet because we can train a good teacher on ImageNet using label data. Med. Astrophysical Observatory. Use Git or checkout with SVN using the web URL. 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. 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. For more information about the large architectures, please refer to Table7 in Appendix A.1. 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 architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. 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. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. The ONCE (One millioN sCenEs) dataset for 3D object detection in the autonomous driving scenario is introduced and a benchmark is provided in which a variety of self-supervised and semi- supervised methods on the ONCE dataset are evaluated. 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. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. A. Krizhevsky, I. Sutskever, and G. E. Hinton, Temporal ensembling for semi-supervised learning, Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks, Workshop on Challenges in Representation Learning, ICML, Certainty-driven consistency loss for semi-supervised learning, C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, R. G. Lopes, D. Yin, B. Poole, J. Gilmer, and E. D. Cubuk, Improving robustness without sacrificing accuracy with patch gaussian augmentation, Y. Luo, J. Zhu, M. Li, Y. Ren, and B. Zhang, Smooth neighbors on teacher graphs for semi-supervised learning, L. Maale, C. K. Snderby, S. K. Snderby, and O. Winther, A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, Towards deep learning models resistant to adversarial attacks, D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten, Exploring the limits of weakly supervised pretraining, T. Miyato, S. Maeda, S. Ishii, and M. Koyama, Virtual adversarial training: a regularization method for supervised and semi-supervised learning, IEEE transactions on pattern analysis and machine intelligence, A. Najafi, S. Maeda, M. Koyama, and T. Miyato, Robustness to adversarial perturbations in learning from incomplete data, J. Ngiam, D. Peng, V. Vasudevan, S. Kornblith, Q. V. Le, and R. Pang, Robustness properties of facebooks resnext wsl models, Adversarial dropout for supervised and semi-supervised learning, Lessons from building acoustic models with a million hours of speech, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), S. Qiao, W. Shen, Z. Zhang, B. Wang, and A. Yuille, Deep co-training for semi-supervised image recognition, I. Radosavovic, P. Dollr, R. Girshick, G. Gkioxari, and K. He, Data distillation: towards omni-supervised learning, A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, Semi-supervised learning with ladder networks, E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, Proceedings of the AAAI Conference on Artificial Intelligence, B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. We iterate this process by putting back the student as the teacher. 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. 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. Their noise model is video specific and not relevant for image classification. 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. Self-training with Noisy Student improves ImageNet classification On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . The accuracy is improved by about 10% in most settings. 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. Agreement NNX16AC86A, Is ADS down? Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. If nothing happens, download GitHub Desktop and try again. 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 This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. 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. Self-Training With Noisy Student Improves ImageNet Classification Self-Training With Noisy Student Improves ImageNet Classification The abundance of data on the internet is vast. 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. ImageNet . The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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. 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 obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. labels, the teacher is not noised so that the pseudo labels are as good as The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Self-Training with Noisy Student Improves ImageNet Classification Noise Self-training with Noisy Student 1. 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. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. 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. 27.8 to 16.1. Our procedure went as follows. 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. 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. 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. Self-Training Noisy Student " " Self-Training . 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]. There was a problem preparing your codespace, please try again. ; 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. 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. Finally, in the above, we say that the pseudo labels can be soft or hard. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Self-training with Noisy Student. In contrast, the predictions of the model with Noisy Student remain quite stable. 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. Add a Self-training with Noisy Student improves ImageNet classification 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 we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. Flip probability is the probability that the model changes top-1 prediction for different perturbations. (using extra training data). Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. This is probably because it is harder to overfit the large unlabeled dataset. Train a classifier on labeled data (teacher). [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . Self-mentoring: : A new deep learning pipeline to train a self 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). ImageNet-A top-1 accuracy from 16.6 Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. IEEE Trans. We also study the effects of using different amounts of unlabeled data. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. possible. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. It can be seen that masks are useful in improving classification performance. To achieve this result, we first train an EfficientNet model on labeled Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. It implements SemiSupervised Learning with Noise to create an Image Classification. 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. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. First, we run an EfficientNet-B0 trained on ImageNet[69]. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. We then perform data filtering and balancing on this corpus. 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.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. 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. For unlabeled images, we set the batch size to be three times the batch size of labeled images for large models, including EfficientNet-B7, L0, L1 and L2. The performance consistently drops with noise function removed. Self-training 1 2Self-training 3 4n What is Noisy Student? unlabeled images , . Work fast with our official CLI. The main use case of knowledge distillation is model compression by making the student model smaller. 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. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model The most interesting image is shown on the right of the first row. Summarization_self-training_with_noisy_student_improves_imagenet_classification. For classes where we have too many images, we take the images with the highest confidence. Infer labels on a much larger unlabeled dataset. 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. 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. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Self-training with Noisy Student improves ImageNet classification In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. We then select images that have confidence of the label higher than 0.3. 10687-10698). Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 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. In other words, the student is forced to mimic a more powerful ensemble model. . Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. 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. . If nothing happens, download GitHub Desktop and try again. 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.

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