Are labels required for improving adversarial robustness? 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. Finally, in the above, we say that the pseudo labels can be soft or hard. 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. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. 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. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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. It is found that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. Yalniz et al. We used the version from [47], which filtered the validation set of ImageNet. (using extra training data). Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. We use EfficientNet-B4 as both the teacher and the student. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. On . We then use the teacher model to generate pseudo labels on unlabeled images. The performance drops when we further reduce it. 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. Similar to[71], we fix the shallow layers during finetuning. In typical self-training with the teacher-student framework, noise injection to the student is not used by default, or the role of noise is not fully understood or justified. 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. 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. However, manually annotating organs from CT scans is time . 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. We will then show our results on ImageNet and compare them with state-of-the-art models. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. It 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. Self-training 1 2Self-training 3 4n What is Noisy Student? We do not tune these hyperparameters extensively since our method is highly robust to them. 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. For RandAugment, we apply two random operations with the magnitude set to 27. Notice, Smithsonian Terms of This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. . During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. Noisy Student self-training is an effective way to leverage unlabelled datasets and improving accuracy by adding noise to the student model while training so it learns beyond the teacher's knowledge. 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. It implements SemiSupervised Learning with Noise to create an Image Classification. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. Here we study how to effectively use out-of-domain data. Self-training with Noisy Student improves ImageNet classification 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. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. For each class, we select at most 130K images that have the highest confidence. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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 score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. For instance, on the right column, as the image of the car undergone a small rotation, the standard model changes its prediction from racing car to car wheel to fire engine. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. 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. 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. You signed in with another tab or window. Self-training with Noisy Student improves ImageNet classification. A semi-supervised segmentation network based on noisy student learning 3429-3440. . This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Infer labels on a much larger unlabeled dataset. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. We use the standard augmentation instead of RandAugment in this experiment. Train a classifier on labeled data (teacher). As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 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. A. Alemi, Thirty-First AAAI Conference on Artificial Intelligence, C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, EfficientNet: rethinking model scaling for convolutional neural networks, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, A. Vedaldi, M. Douze, and H. Jgou, Fixing the train-test resolution discrepancy, V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), J. Weston, F. Ratle, H. Mobahi, and R. Collobert, Deep learning via semi-supervised embedding, Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le, Unsupervised data augmentation for consistency training, S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, I. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. But training robust supervised learning models is requires this step. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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 - Medium Use Git or checkout with SVN using the web URL. Test images on ImageNet-P underwent different scales of perturbations. 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. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. Self-Training With Noisy Student Improves ImageNet Classification over the JFT dataset to predict a label for each image. [68, 24, 55, 22]. Learn more. GitHub - google-research/noisystudent: Code for Noisy Student Training Self-Training : Noisy Student : Self-training with Noisy Student improves ImageNet classification 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. Their main goal is to find a small and fast model for deployment. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. 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. Self-training with Noisy Student improves ImageNet classification We iterate this process by putting back the student as the teacher. Self-Training for Natural Language Understanding! This material is presented to ensure timely dissemination of scholarly and technical work. augmentation, dropout, stochastic depth to the student so that the noised We iterate this process by putting back the student as the teacher. (or is it just me), Smithsonian Privacy Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Self-training with noisy student improves imagenet classification. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. We sample 1.3M images in confidence intervals. Code is available at https://github.com/google-research/noisystudent. 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. 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. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. 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 apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. During this process, we kept increasing the size of the student model to improve the performance. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. The inputs to the algorithm are both labeled and unlabeled images. We duplicate images in classes where there are not enough images. If nothing happens, download GitHub Desktop and try again. Le. Self-training with Noisy Student improves ImageNet classification Our main results are shown in Table1. Noisy Student Training seeks to improve on self-training and distillation in two ways. Different kinds of noise, however, may have different effects. unlabeled images , . Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. With Noisy Student, the model correctly predicts dragonfly for the image. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). We apply dropout to the final classification layer with a dropout rate of 0.5. , have shown that computer vision models lack robustness. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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 . 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. w Summary of key results compared to previous state-of-the-art models. 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]). Summarization_self-training_with_noisy_student_improves_imagenet CVPR 2020 Open Access Repository 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. Self-training with Noisy Student improves ImageNet classification. 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]. Self-Training With Noisy Student Improves ImageNet Classification Work fast with our official CLI. Self-Training With Noisy Student Improves ImageNet Classification A tag already exists with the provided branch name. combination of labeled and pseudo labeled images. Use Git or checkout with SVN using the web URL. 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. student is forced to learn harder from the pseudo labels. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. 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. In the following, we will first describe experiment details to achieve our results. 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. 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. These CVPR 2020 papers are the Open Access versions, provided by the. First, we run an EfficientNet-B0 trained on ImageNet[69]. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. 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. Their noise model is video specific and not relevant for image classification. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Imaging, 39 (11) (2020), pp. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. to use Codespaces. . We iterate this process by putting back the student as the teacher. all 12, 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. The abundance of data on the internet is vast. self-mentoring outperforms data augmentation and self training. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 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. 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. The performance consistently drops with noise function removed. There was a problem preparing your codespace, please try again. Learn more. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 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. Self-Training With Noisy Student Improves ImageNet Classification 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. As can be seen from the figure, our model with Noisy Student makes correct predictions for images under severe corruptions and perturbations such as snow, motion blur and fog, while the model without Noisy Student suffers greatly under these conditions. 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]. 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.
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