self-mentoring outperforms data augmentation and self training. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. First, we run an EfficientNet-B0 trained on ImageNet[69]. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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. Noisy Students performance improves with more unlabeled data. 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. 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. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. 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. 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. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. 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. 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. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . 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. . We use a resolution of 800x800 in this experiment. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. If nothing happens, download Xcode and try again. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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. 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. On . all 12, Image Classification 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. Different types of. 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. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: For ImageNet checkpoints trained by Noisy Student Training, please refer to the EfficientNet github. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. Train a larger classifier on the combined set, adding noise (noisy student). Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. Due to duplications, there are only 81M unique images among these 130M images. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Copyright and all rights therein are retained by authors or by other copyright holders. - : self-training_with_noisy_student_improves_imagenet_classification Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. . As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. 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. Test images on ImageNet-P underwent different scales of perturbations. The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. During the generation of the pseudo Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). In particular, we first perform normal training with a smaller resolution for 350 epochs. Self-training with Noisy Student improves ImageNet classification. 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. But during the learning of the student, we inject noise such as data We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. Their purpose is different from ours: to adapt a teacher model on one domain to another. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). For each class, we select at most 130K images that have the highest confidence. 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. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Our procedure went as follows. w Summary of key results compared to previous state-of-the-art models. Iterative training is not used here for simplicity. If nothing happens, download Xcode and try again. 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. 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 is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Noisy Student Training is based on the self-training framework and trained with 4-simple steps: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. student is forced to learn harder from the pseudo labels. If nothing happens, download GitHub Desktop and try again. 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. Next, a larger student model is trained on the combination of all data and achieves better performance than the teacher by itself.OUTLINE:0:00 - Intro \u0026 Overview1:05 - Semi-Supervised \u0026 Transfer Learning5:45 - Self-Training \u0026 Knowledge Distillation10:00 - Noisy Student Algorithm Overview20:20 - Noise Methods22:30 - Dataset Balancing25:20 - Results30:15 - Perturbation Robustness34:35 - Ablation Studies39:30 - Conclusion \u0026 CommentsPaper: https://arxiv.org/abs/1911.04252Code: https://github.com/google-research/noisystudentModels: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnetAbstract:We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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 is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks. 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. (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. 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. A tag already exists with the provided branch name. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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 The architectures for the student and teacher models can be the same or different. 10687-10698). The abundance of data on the internet is vast. Semi-supervised medical image classification with relation-driven self-ensembling model. Astrophysical Observatory. Noisy Student Training is a semi-supervised learning approach. In contrast, the predictions of the model with Noisy Student remain quite stable. For more information about the large architectures, please refer to Table7 in Appendix A.1. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. A number of studies, e.g. We then select images that have confidence of the label higher than 0.3. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. It implements SemiSupervised Learning with Noise to create an Image Classification. Please Self-training 1 2Self-training 3 4n What is Noisy Student? over the JFT dataset to predict a label for each image. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. Edit social preview. Here we study how to effectively use out-of-domain data. 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. 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. Their noise model is video specific and not relevant for image classification. A tag already exists with the provided branch name. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). 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. Please refer to [24] for details about mCE and AlexNets error rate. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Self-training with Noisy Student improves ImageNet classification Abstract. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Noisy Student Training seeks to improve on self-training and distillation in two ways. In this section, we study the importance of noise and the effect of several noise methods used in our model. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Noisy Student leads to significant improvements across all model sizes for EfficientNet. We iterate this process by putting back the student as the teacher. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2.