self training with noisy student improves imagenet classification

all 12, Image Classification . Especially unlabeled images are plentiful and can be collected with ease. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. Noisy StudentImageNetEfficientNet-L2state-of-the-art. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. Code for Noisy Student Training. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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. We also list EfficientNet-B7 as a reference. Our study shows that using unlabeled data improves accuracy and general robustness. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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: Train a classifier on labeled data (teacher). We do not tune these hyperparameters extensively since our method is highly robust to them. . self-mentoring outperforms data augmentation and self training. Code for Noisy Student Training. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. These CVPR 2020 papers are the Open Access versions, provided by the. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. , have shown that computer vision models lack robustness. https://arxiv.org/abs/1911.04252. Then, that teacher is used to label the unlabeled data. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. This model investigates a new method. 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. To achieve this result, we first train an EfficientNet model on labeled Please Noisy Student Training is a semi-supervised learning approach. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. 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. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. We use the labeled images to train a teacher model using the standard cross entropy loss. Their purpose is different from ours: to adapt a teacher model on one domain to another. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. 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. 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. If nothing happens, download Xcode and try again. For each class, we select at most 130K images that have the highest confidence. 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 framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. 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. . 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. 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. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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. As shown in Table2, Noisy Student with EfficientNet-L2 achieves 87.4% top-1 accuracy which is significantly better than the best previously reported accuracy on EfficientNet of 85.0%. Astrophysical Observatory. We improved it by adding noise to the student to learn beyond the teachers knowledge. sign in 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. On robustness test sets, it improves ImageNet-A top . 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. 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. 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. For instance, on ImageNet-A, Noisy Student achieves 74.2% top-1 accuracy which is approximately 57% more accurate than the previous state-of-the-art model. Are you sure you want to create this branch? Please For RandAugment, we apply two random operations with the magnitude set to 27. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Self-training with Noisy Student improves ImageNet classification. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. We use EfficientNet-B4 as both the teacher and the student. 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. This is probably because it is harder to overfit the large unlabeled dataset. Chowdhury et al. Noise Self-training with Noisy Student 1. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. If nothing happens, download GitHub Desktop and try again. On . 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. Agreement NNX16AC86A, Is ADS down? 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. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. 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. ImageNet images and use it as a teacher to generate pseudo labels on 300M 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. Do better imagenet models transfer better? But training robust supervised learning models is requires this step. 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. 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. 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. These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. We apply dropout to the final classification layer with a dropout rate of 0.5. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. 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 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]. E. Arazo, D. Ortego, P. Albert, N. E. OConnor, and K. McGuinness, Pseudo-labeling and confirmation bias in deep semi-supervised learning, B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson, There are many consistent explanations of unlabeled data: why you should average, International Conference on Learning Representations, Advances in Neural Information Processing Systems, D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, MixMatch: a holistic approach to semi-supervised learning, Combining labeled and unlabeled data with co-training, C. Bucilu, R. Caruana, and A. Niculescu-Mizil, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Y. Carmon, A. Raghunathan, L. Schmidt, P. Liang, and J. C. Duchi, Unlabeled data improves adversarial robustness, Semi-supervised learning (chapelle, o. et al., eds. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Ranked #14 on Le. possible. 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 use a resolution of 800x800 in this experiment. 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. 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. 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. 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. We then train a larger EfficientNet as a student model on the The baseline model achieves an accuracy of 83.2. The algorithm is basically self-training, a method in semi-supervised learning (. If nothing happens, download GitHub Desktop and try again. There was a problem preparing your codespace, please try again. In contrast, the predictions of the model with Noisy Student remain quite stable. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. on ImageNet ReaL 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). supervised model from 97.9% accuracy to 98.6% accuracy. The main use case of knowledge distillation is model compression by making the student model smaller. [57] used self-training for domain adaptation. Self-Training with Noisy Student Improves ImageNet Classification Finally, in the above, we say that the pseudo labels can be soft or hard. We use the same architecture for the teacher and the student and do not perform iterative training. Parthasarathi et al. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Different types of. 10687-10698 Abstract First, a teacher model is trained in a supervised fashion. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. 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Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. During this process, we kept increasing the size of the student model to improve the performance. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. Code is available at https://github.com/google-research/noisystudent. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. IEEE Trans. [^reference-9] [^reference-10] A critical insight was to . The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. Learn more. 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. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Please refer to [24] for details about mFR and AlexNets flip probability. 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. On, International journal of molecular sciences. Self-Training Noisy Student " " Self-Training . 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. 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. - : 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. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. sign in 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]. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. The abundance of data on the internet is vast. 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. 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. 3429-3440. . We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. We iterate this process by putting back the student as the teacher. 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. Different kinds of noise, however, may have different effects. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. If nothing happens, download Xcode and try again. 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). After using the masks generated by teacher-SN, the classification performance improved by 0.2 of AC, 1.2 of SP, and 0.7 of AUC. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. Train a classifier on labeled data (teacher). Here we study if it is possible to improve performance on small models by using a larger teacher model, since small models are useful when there are constraints for model size and latency in real-world applications. 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. Our main results are shown in Table1. 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. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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. combination of labeled and pseudo labeled images. Due to duplications, there are only 81M unique images among these 130M images. A. 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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. We used the version from [47], which filtered the validation set of ImageNet. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. 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. Use Git or checkout with SVN using the web URL. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . Image Classification The performance consistently drops with noise function removed. We start with the 130M unlabeled images and gradually reduce the number of images. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks. labels, the teacher is not noised so that the pseudo labels are as good as 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. Imaging, 39 (11) (2020), pp. 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 . By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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.

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