Self-training with Noisy Student - Medium Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. Astrophysical Observatory. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. CLIP (Contrastive Language-Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.The idea of zero-data learning dates back over a decade [^reference-8] but until recently was mostly studied in computer vision as a way of generalizing to unseen object categories. The performance drops when we further reduce it. On . 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. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from Noisy Student Explained | Papers With Code Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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 then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled 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. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. Self-training with Noisy Student improves ImageNet classification. Self-training with Noisy Student improves ImageNet classification to use Codespaces. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. There was a problem preparing your codespace, please try again. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. Add a 10687-10698). labels, the teacher is not noised so that the pseudo labels are as good as The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. over the JFT dataset to predict a label for each image. ImageNet-A top-1 accuracy from 16.6 The accuracy is improved by about 10% in most settings. 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. 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. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. In other words, small changes in the input image can cause large changes to the predictions. 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. IEEE Trans. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin If nothing happens, download Xcode and try again. Here we show the evidence in Table 6, noise such as stochastic depth, dropout and data augmentation plays an important role in enabling the student model to perform better than the teacher. However, manually annotating organs from CT scans is time . Similar to[71], we fix the shallow layers during finetuning. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. 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. For more information about the large architectures, please refer to Table7 in Appendix A.1. Self-training with Noisy Student. We use the same architecture for the teacher and the student and do not perform iterative training. 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]. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. (using extra training data). 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. Self-training Self-training with Noisy Student improves ImageNet classification In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. and surprising gains on robustness and adversarial benchmarks. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 w Summary of key results compared to previous state-of-the-art models. Self-training with noisy student improves imagenet classification. 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. Self-Training for Natural Language Understanding! C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Their purpose is different from ours: to adapt a teacher model on one domain to another. [57] used self-training for domain adaptation. Here we study how to effectively use out-of-domain data. Abdominal organ segmentation is very important for clinical applications. It can be seen that masks are useful in improving classification performance. It implements SemiSupervised Learning with Noise to create an Image Classification. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. We also list EfficientNet-B7 as a reference. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. 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]. all 12, Image Classification In contrast, the predictions of the model with Noisy Student remain quite stable. Self-training with Noisy Student improves ImageNet classification Use Git or checkout with SVN using the web URL. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. When the student model is deliberately noised it is actually trained to be consistent to the more powerful teacher model that is not noised when it generates pseudo labels. Self-training with Noisy Student improves ImageNet classification PDF Self-Training with Noisy Student Improves ImageNet Classification 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. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. 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. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards Our work is based on self-training (e.g.,[59, 79, 56]). Noisy Student self-training is an effective way to leverage unlabelled datasets and improving accuracy by adding noise to the student model while training so it learns beyond the teacher's knowledge. This material is presented to ensure timely dissemination of scholarly and technical work. This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. Med. Noise Self-training with Noisy Student 1. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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]. Summarization_self-training_with_noisy_student_improves_imagenet This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. Hence we use soft pseudo labels for our experiments unless otherwise specified. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. 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. Are you sure you want to create this branch? 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 . Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. CLIP: Connecting text and images - OpenAI 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. Edit social preview. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen, GPipe: efficient training of giant neural networks using pipeline parallelism, A. Iscen, G. Tolias, Y. Avrithis, and O. 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. We then use the teacher model to generate pseudo labels on unlabeled images. First, we run an EfficientNet-B0 trained on ImageNet[69]. Especially unlabeled images are plentiful and can be collected with ease. In this section, we study the importance of noise and the effect of several noise methods used in our model. In particular, we first perform normal training with a smaller resolution for 350 epochs. Self-Training With Noisy Student Improves ImageNet Classification 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 used the version from [47], which filtered the validation set of ImageNet. student is forced to learn harder from the pseudo labels. Flip probability is the probability that the model changes top-1 prediction for different perturbations. Self-Training : Noisy Student : Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. The architectures for the student and teacher models can be the same or different. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. GitHub - google-research/noisystudent: Code for Noisy Student Training If nothing happens, download GitHub Desktop and try again. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . These CVPR 2020 papers are the Open Access versions, provided by the. ImageNet images and use it as a teacher to generate pseudo labels on 300M 10687-10698 Abstract Learn more. We iterate this process by putting back the student as the teacher. But during the learning of the student, we inject noise such as data 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. Imaging, 39 (11) (2020), pp. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Noisy Student can still improve the accuracy to 1.6%. putting back the student as the teacher. See If you get a better model, you can use the model to predict pseudo-labels on the filtered data. We improved it by adding noise to the student to learn beyond the teachers knowledge. 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. Yalniz et al. . Self-training with Noisy Student improves ImageNet classification Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. 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. Distillation Survey : Noisy Student | 9to5Tutorial We use the standard augmentation instead of RandAugment in this experiment. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single model. 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. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Please refer to [24] for details about mFR and AlexNets flip probability. 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 addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. During this process, we kept increasing the size of the student model to improve the performance. Self-training with Noisy Student improves ImageNet classification 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 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). The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. The most interesting image is shown on the right of the first row. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Iterative training is not used here for simplicity. 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. You signed in with another tab or window. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. Self-training with Noisy Student improves ImageNet classificationCVPR2020, Codehttps://github.com/google-research/noisystudent, Self-training, 1, 2Self-training, Self-trainingGoogleNoisy Student, Noisy Studentstudent modeldropout, stochastic depth andaugmentationteacher modelNoisy Noisy Student, Noisy Student, 1, JFT3ImageNetEfficientNet-B00.3130K130K, EfficientNetbaseline modelsEfficientNetresnet, EfficientNet-B7EfficientNet-L0L1L2, batchsize = 2048 51210242048EfficientNet-B4EfficientNet-L0l1L2350epoch700epoch, 2EfficientNet-B7EfficientNet-L0, 3EfficientNet-L0EfficientNet-L1L0, 4EfficientNet-L1EfficientNet-L2, student modelNoisy, noisystudent modelteacher modelNoisy, Noisy, Self-trainingaugmentationdropoutstochastic depth, Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores., 12/self-training-with-noisy-student-f33640edbab2, EfficientNet-L0EfficientNet-B7B7, EfficientNet-L1EfficientNet-L0, EfficientNetsEfficientNet-L1EfficientNet-L2EfficientNet-L2EfficientNet-B75. unlabeled images , . 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%. [^reference-9] [^reference-10] A critical insight was to . The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Self-training with Noisy Student improves ImageNet classification 2023.3.1_2 - [68, 24, 55, 22]. 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. 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. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. On robustness test sets, it improves ImageNet-A top . 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. Classification of Socio-Political Event Data, SLADE: A Self-Training Framework For Distance Metric Learning, Self-Training with Differentiable Teacher, https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). If nothing happens, download Xcode and try again. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. 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. International Conference on Machine Learning, Learning extraction patterns for subjective expressions, Proceedings of the 2003 conference on Empirical methods in natural language processing, A. Roy Chowdhury, P. Chakrabarty, A. Singh, S. Jin, H. Jiang, L. Cao, and E. G. Learned-Miller, Automatic adaptation of object detectors to new domains using self-training, T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, Probability of error of some adaptive pattern-recognition machines, W. Shi, Y. Gong, C. Ding, Z. MaXiaoyu Tao, and N. Zheng, Transductive semi-supervised deep learning using min-max features, C. Simon-Gabriel, Y. Ollivier, L. Bottou, B. Schlkopf, and D. Lopez-Paz, First-order adversarial vulnerability of neural networks and input dimension, Very deep convolutional networks for large-scale image recognition, N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. 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 The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). With Noisy Student, the model correctly predicts dragonfly for the image. 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. Please refer to [24] for details about mCE and AlexNets error rate. We use a resolution of 800x800 in this experiment. on ImageNet, which is 1.0 The main use case of knowledge distillation is model compression by making the student model smaller. 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. 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. We start with the 130M unlabeled images and gradually reduce the number of images. unlabeled images. We use the labeled images to train a teacher model using the standard cross entropy loss. Why Self-training with Noisy Students beats SOTA Image classification Noisy Students performance improves with more unlabeled data. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Self-Training with Noisy Student Improves ImageNet Classification Self-Training With Noisy Student Improves ImageNet Classification Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. For simplicity, we experiment with using 1128,164,132,116,14 of the whole data by uniformly sampling images from the the unlabeled set though taking the images with highest confidence leads to better results. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. In the following, we will first describe experiment details to achieve our results. We sample 1.3M images in confidence intervals. 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. possible. Self-training with Noisy Student improves ImageNet classification