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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. We iterate this process by putting back the student as the teacher. Are you sure you want to create this branch? Code is available at https://github.com/google-research/noisystudent. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . 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. Ranked #14 on The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Our work is based on self-training (e.g.,[59, 79, 56]). Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. student is forced to learn harder from the pseudo labels. 10687-10698 Abstract unlabeled images. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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. 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. Code for Noisy Student Training. on ImageNet ReaL. 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. 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. Med. et al. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 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 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. 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. Noisy Student can still improve the accuracy to 1.6%. 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. Please refer to [24] for details about mFR and AlexNets flip probability. 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. A common workaround is to use entropy minimization or ramp up the consistency loss. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. To achieve this result, we first train an EfficientNet model on labeled Figure 1(c) shows images from ImageNet-P and the corresponding predictions. Further, Noisy Student outperforms the state-of-the-art accuracy of 86.4% by FixRes ResNeXt-101 WSL[44, 71] that requires 3.5 Billion Instagram images labeled with tags. 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. Copyright and all rights therein are retained by authors or by other copyright holders. 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. But training robust supervised learning models is requires this step. This paper reviews the state-of-the-art in both the field of CNNs for image classification and object detection and Autonomous Driving Systems (ADSs) in a synergetic way including a comprehensive trade-off analysis from a human-machine perspective. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. (Submitted on 11 Nov 2019) We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. Work fast with our official CLI. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Specifically, as all classes in ImageNet have a similar number of labeled images, we also need to balance the number of unlabeled images for each class. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. A tag already exists with the provided branch name. Finally, in the above, we say that the pseudo labels can be soft or hard. Self-training with noisy student improves imagenet classification. Learn more. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. By clicking accept or continuing to use the site, you agree to the terms outlined in our. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. Then, that teacher is used to label the unlabeled data. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Notice, Smithsonian Terms of However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. 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. Semi-supervised medical image classification with relation-driven self-ensembling model. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. The results also confirm that vision models can benefit from Noisy Student even without iterative training. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. Test images on ImageNet-P underwent different scales of perturbations. Image Classification Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. On, International journal of molecular sciences. The algorithm is basically self-training, a method in semi-supervised learning (. 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]. If nothing happens, download GitHub Desktop and try again. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Learn more. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). But during the learning of the student, we inject noise such as data 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. ImageNet . Use Git or checkout with SVN using the web URL. 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. w Summary of key results compared to previous state-of-the-art models. 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. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. Self-Training With Noisy Student Improves ImageNet Classification. Our study shows that using unlabeled data improves accuracy and general robustness. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. . Models are available at this https URL. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. ImageNet images and use it as a teacher to generate pseudo labels on 300M During this process, we kept increasing the size of the student model to improve the performance. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. 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. The baseline model achieves an accuracy of 83.2. 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. It implements SemiSupervised Learning with Noise to create an Image Classification. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. We then train a larger EfficientNet as a student model on the We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Please refer to [24] for details about mCE and AlexNets error rate. 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. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. 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. 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. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Self-Training Noisy Student " " Self-Training . Although they have produced promising results, in our preliminary experiments, consistency regularization works less well on ImageNet because consistency regularization in the early phase of ImageNet training regularizes the model towards high entropy predictions, and prevents it from achieving good accuracy. 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. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. 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. IEEE Trans. The inputs to the algorithm are both labeled and unlabeled images. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. This result is also a new state-of-the-art and 1% better than the previous best method that used an order of magnitude more weakly labeled data [ 44, 71]. Imaging, 39 (11) (2020), pp. augmentation, dropout, stochastic depth to the student so that the noised 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. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. 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. In other words, small changes in the input image can cause large changes to the predictions. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. 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. 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. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. Infer labels on a much larger unlabeled dataset. 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. Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. 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). 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. 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. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. With Noisy Student, the model correctly predicts dragonfly for the image. 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 investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Self-training with Noisy Student improves ImageNet classification. Different kinds of noise, however, may have different effects. Le. We then use the teacher model to generate pseudo labels on unlabeled images. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 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. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. For RandAugment, we apply two random operations with the magnitude set to 27. - : self-training_with_noisy_student_improves_imagenet_classification 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. In particular, we first perform normal training with a smaller resolution for 350 epochs. 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. 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. 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. If nothing happens, download Xcode and try again. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. possible. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. 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.