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. 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. 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. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Yalniz et al. 10687-10698). 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. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. 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. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. Do better imagenet models transfer better? w Summary of key results compared to previous state-of-the-art models. Self-training with Noisy Student improves ImageNet classification During this process, we kept increasing the size of the student model to improve the performance. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. 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. Computer Science - Computer Vision and Pattern Recognition. If nothing happens, download Xcode and try again. We then select images that have confidence of the label higher than 0.3. 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. Med. Noisy StudentImageNetEfficientNet-L2state-of-the-art. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. 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]. But during the learning of the student, we inject noise such as data 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. 27.8 to 16.1. 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. Please refer to [24] for details about mCE and AlexNets error rate. 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. 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. . Self-training with Noisy Student improves ImageNet classification Original paper: https://arxiv.org/pdf/1911.04252.pdf Authors: Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le HOYA012 Introduction EfficientNet ImageNet SOTA EfficientNet 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. For each class, we select at most 130K images that have the highest confidence. Imaging, 39 (11) (2020), pp. Especially unlabeled images are plentiful and can be collected with ease. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . Iterative training is not used here for simplicity. Their main goal is to find a small and fast model for deployment. 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. 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 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. over the JFT dataset to predict a label for each image. 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. 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. If nothing happens, download GitHub Desktop and try again. Self-training with noisy student improves imagenet classification. 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. In contrast, the predictions of the model with Noisy Student remain quite stable. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. CVPR 2020 Open Access Repository We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. 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. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . We present a simple self-training method that achieves 87.4 This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. . Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. Flip probability is the probability that the model changes top-1 prediction for different perturbations. 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. 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. Self-training with Noisy Student improves ImageNet classification Distillation Survey : Noisy Student | 9to5Tutorial 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. 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. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. Our work is based on self-training (e.g.,[59, 79, 56]). 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 Proceedings of the eleventh annual conference on Computational learning theory, Proceedings of the IEEE conference on computer vision and pattern recognition, Empirical Methods in Natural Language Processing (EMNLP), Imagenet classification with deep convolutional neural networks, Domain adaptive transfer learning with specialist models, Thirty-Second AAAI Conference on Artificial Intelligence, Regularized evolution for image classifier architecture search, Inception-v4, inception-resnet and the impact of residual connections on learning. 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 use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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]. The abundance of data on the internet is vast. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. Self-training with Noisy Student improves ImageNet classification Code for Noisy Student Training. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. to noise the student. Self-training with Noisy Student - 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. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. , have shown that computer vision models lack robustness. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Train a classifier on labeled data (teacher). In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. Chowdhury et al. 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. [57] used self-training for domain adaptation. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). 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. 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. Please 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. 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. (using extra training data). Noisy Student Explained | Papers With Code Soft pseudo labels lead to better performance for low confidence data. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical A tag already exists with the provided branch name. Train a larger classifier on the combined set, adding noise (noisy student). This is probably because it is harder to overfit the large unlabeled dataset. Self-training with Noisy Student improves ImageNet classification. Finally, in the above, we say that the pseudo labels can be soft or hard. Whether the model benefits from more unlabeled data depends on the capacity of the model since a small model can easily saturate, while a larger model can benefit from more data. The accuracy is improved by about 10% in most settings. Agreement NNX16AC86A, Is ADS down? Self-training with Noisy Student improves ImageNet classication Qizhe Xie 1, Minh-Thang Luong , Eduard Hovy2, Quoc V. Le1 1Google Research, Brain Team, 2Carnegie Mellon University fqizhex, thangluong, qvlg@google.com, hovy@cmu.edu Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . Train a classifier on labeled data (teacher). 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. Self-training with Noisy Student improves ImageNet classification. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. . We determine number of training steps and the learning rate schedule by the batch size for labeled images. We use a resolution of 800x800 in this experiment. 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. 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. Do imagenet classifiers generalize to imagenet? Are you sure you want to create this branch? The abundance of data on the internet is vast. 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. 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 has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images We then perform data filtering and balancing on this corpus. The performance consistently drops with noise function removed. 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. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 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. We then train a larger EfficientNet as a student model on the Use, Smithsonian The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative To achieve this result, we first train an EfficientNet model on labeled On . 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. 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. Self-training As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. 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 iterate this process by putting back the student as the teacher. The algorithm is basically self-training, a method in semi-supervised learning (. 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. The comparison is shown in Table 9. FixMatch-LS: Semi-supervised skin lesion classification with label 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. 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. 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. In other words, the student is forced to mimic a more powerful ensemble model. 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%. If nothing happens, download Xcode and try again. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. It is expensive and must be done with great care. 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. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Self-training with Noisy Student improves ImageNet classification Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. task. We use EfficientNet-B4 as both the teacher and the student. 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. 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. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Are labels required for improving adversarial robustness? [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. on ImageNet, which is 1.0 Test images on ImageNet-P underwent different scales of perturbations. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Works based on pseudo label[37, 31, 60, 1] are similar to self-training, but also suffers the same problem with consistency training, since it relies on a model being trained instead of a converged model with high accuracy to generate pseudo labels. 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Code is available at https://github.com/google-research/noisystudent. all 12, Image Classification Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. On, International journal of molecular sciences. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). ImageNet-A top-1 accuracy from 16.6 3.5B weakly labeled Instagram images.