self training with noisy student improves imagenet classification

Specifically, we train the student model for 350 epochs for models larger than EfficientNet-B4, including EfficientNet-L0, L1 and L2 and train the student model for 700 epochs for smaller models. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . 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. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 27.8 to 16.1. 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. 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. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . 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. This material is presented to ensure timely dissemination of scholarly and technical work. 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. To date (2020) we will introduce "Noisy Student Training", which is a state-of-the-art model.The idea is to extend self-training and Distillation, a paper that shows that by adding three noises and distilling multiple times, the student model will have better generalization performance than the teacher model. We improved it by adding noise to the student to learn beyond the teachers knowledge. We thank the Google Brain team, Zihang Dai, Jeff Dean, Hieu Pham, Colin Raffel, Ilya Sutskever and Mingxing Tan for insightful discussions, Cihang Xie for robustness evaluation, Guokun Lai, Jiquan Ngiam, Jiateng Xie and Adams Wei Yu for feedbacks on the draft, Yanping Huang and Sameer Kumar for improving TPU implementation, Ekin Dogus Cubuk and Barret Zoph for help with RandAugment, Yanan Bao, Zheyun Feng and Daiyi Peng for help with the JFT dataset, Olga Wichrowska and Ola Spyra for help with infrastructure. 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. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. The accuracy is improved by about 10% in most settings. For RandAugment, we apply two random operations with the magnitude set to 27. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical unlabeled 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. 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]. labels, the teacher is not noised so that the pseudo labels are as good as It implements SemiSupervised Learning with Noise to create an Image Classification. Noisy StudentImageNetEfficientNet-L2state-of-the-art. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. We sample 1.3M images in confidence intervals. This model investigates a new method. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. [^reference-9] [^reference-10] A critical insight was to . The comparison is shown in Table 9. Imaging, 39 (11) (2020), pp. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images 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 iterate this process by 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 then train a larger EfficientNet as a student model on the 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 . 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). 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. 10687-10698 Abstract The main use case of knowledge distillation is model compression by making the student model smaller. task. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. During the generation of the pseudo . This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. 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. 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 We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le Description: 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. Train a larger classifier on the combined set, adding noise (noisy student). to use Codespaces. Their main goal is to find a small and fast model for deployment. Our work is based on self-training (e.g.,[59, 79, 56]). But during the learning of the student, we inject noise such as data Self-training 1 2Self-training 3 4n What is Noisy Student? Do imagenet classifiers generalize to imagenet? We iterate this process by putting back the student as the teacher. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. . Please refer to [24] for details about mCE and AlexNets error rate. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. We use EfficientNet-B4 as both the teacher and the student. 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. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 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. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. The results also confirm that vision models can benefit from Noisy Student even without iterative training. augmentation, dropout, stochastic depth to the student so that the noised In other words, the student is forced to mimic a more powerful ensemble model. Self-Training With Noisy Student Improves ImageNet Classification. on ImageNet, which is 1.0 Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. 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 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. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Noisy Student can still improve the accuracy to 1.6%. Code is available at https://github.com/google-research/noisystudent. If nothing happens, download GitHub Desktop and try again. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Self-Training Noisy Student " " Self-Training . 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. 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. If nothing happens, download Xcode and try again. On, International journal of molecular sciences. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Summarization_self-training_with_noisy_student_improves_imagenet_classification. There was a problem preparing your codespace, please try again. 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]. 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 . Use Git or checkout with SVN using the web URL. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Are labels required for improving adversarial robustness? Our experiments show that an important element for this simple method to work well at scale is that the student model should be noised during its training while the teacher should not be noised during the generation of pseudo labels. The algorithm is basically self-training, a method in semi-supervised learning (. Flip probability is the probability that the model changes top-1 prediction for different perturbations. 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. 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. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. With Noisy Student, the model correctly predicts dragonfly for the image. We apply dropout to the final classification layer with a dropout rate of 0.5. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Work fast with our official CLI. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. Infer labels on a much larger unlabeled dataset. . 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. 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. 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. A novel random matrix theory based damping learner for second order optimisers inspired by linear shrinkage estimation is developed, and it is demonstrated that the derived method works well with adaptive gradient methods such as Adam. It can be seen that masks are useful in improving classification performance. We use the same architecture for the teacher and the student and do not perform iterative training. 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. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Noisy Student Training seeks to improve on self-training and distillation in two ways. 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. While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. 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. ImageNet images and use it as a teacher to generate pseudo labels on 300M EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. ImageNet . The abundance of data on the internet is vast. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. 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. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. 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. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. There was a problem preparing your codespace, please try again. 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]. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. student is forced to learn harder from the pseudo labels. 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. 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 first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. We find that Noisy Student is better with an additional trick: data balancing. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). 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. Are you sure you want to create this branch? Test images on ImageNet-P underwent different scales of perturbations. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. 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. . We then use the teacher model to generate pseudo labels on unlabeled images. 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. on ImageNet ReaL. 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. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. 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. The inputs to the algorithm are both labeled and unlabeled images. to use Codespaces. We also list EfficientNet-B7 as a reference. 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 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. Train a classifier on labeled data (teacher). . This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. [68, 24, 55, 22]. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. 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]. We use a resolution of 800x800 in this experiment. Code for Noisy Student Training. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. You signed in with another tab or window. At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. 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. Noisy Student (B7, L2) means to use EfficientNet-B7 as the student and use our best model with 87.4% accuracy as the teacher model. 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. 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Trans. 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. Due to duplications, there are only 81M unique images among these 130M images. 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. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. 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.

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