self training with noisy student improves imagenet classification

Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. 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. Our study shows that using unlabeled data improves accuracy and general robustness. 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. 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. We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Due to duplications, there are only 81M unique images among these 130M images. 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. 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. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Self-training with Noisy Student improves ImageNet classification. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Learn more. 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 comparison is shown in Table 9. 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. 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. 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. 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. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. on ImageNet, which is 1.0 This material is presented to ensure timely dissemination of scholarly and technical work. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images The accuracy is improved by about 10% in most settings. The abundance of data on the internet is vast. Flip probability is the probability that the model changes top-1 prediction for different perturbations. Soft pseudo labels lead to better performance for low confidence data. 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. We will then show our results on ImageNet and compare them with state-of-the-art models. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. Noisy Student can still improve the accuracy to 1.6%. 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 . 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. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and self-mentoring outperforms data augmentation and self training. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Especially unlabeled images are plentiful and can be collected with ease. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Noisy Student Training is a semi-supervised learning approach. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. The width. 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. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Self-training 1 2Self-training 3 4n What is Noisy Student? Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . 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. Different kinds of noise, however, may have different effects. A novel random matrix theory based damping learner for second order optimisers inspired by linear shrinkage estimation is developed, and it is demonstrated that the derived method works well with adaptive gradient methods such as Adam. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. 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. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. Notice, Smithsonian Terms of Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a Similar to[71], we fix the shallow layers during finetuning. all 12, Image Classification If you get a better model, you can use the model to predict pseudo-labels on the filtered data. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. For each class, we select at most 130K images that have the highest confidence. We sample 1.3M images in confidence intervals. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. The main use case of knowledge distillation is model compression by making the student model smaller. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 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 performance drops when we further reduce it. 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 then use the teacher model to generate pseudo labels on unlabeled images. Self-training was previously used to improve ResNet-50 from 76.4% to 81.2% top-1 accuracy[76] which is still far from the state-of-the-art accuracy. 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. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. A common workaround is to use entropy minimization or ramp up the consistency loss. . Self-training with Noisy Student. Self-training The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative 3.5B weakly labeled Instagram images. 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. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. possible. 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. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. . 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. Hence we use soft pseudo labels for our experiments unless otherwise specified. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Train a classifier on labeled data (teacher). We use a resolution of 800x800 in this experiment. Their main goal is to find a small and fast model for deployment. Learn more. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Work fast with our official CLI. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model To achieve this result, we first train an EfficientNet model on labeled Abdominal organ segmentation is very important for clinical applications. Use Git or checkout with SVN using the web URL. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. If nothing happens, download GitHub Desktop and try again. It implements SemiSupervised Learning with Noise to create an Image Classification. 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. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. 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. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. We first report the validation set accuracy on the ImageNet 2012 ILSVRC challenge prediction task as commonly done in literature[35, 66, 23, 69] (see also [55]). to noise the student. 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 Here we study how to effectively use out-of-domain data. ImageNet images and use it as a teacher to generate pseudo labels on 300M We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. 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. 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. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. 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. In this section, we study the importance of noise and the effect of several noise methods used in our model. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. combination of labeled and pseudo labeled images. We determine number of training steps and the learning rate schedule by the batch size for labeled images. We find that Noisy Student is better with an additional trick: data balancing. 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. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. First, we run an EfficientNet-B0 trained on ImageNet[69]. In other words, the student is forced to mimic a more powerful ensemble model. On robustness test sets, it improves ImageNet-A top . The main difference between our method and knowledge distillation is that knowledge distillation does not consider unlabeled data and does not aim to improve the student model. w Summary of key results compared to previous state-of-the-art models. 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.

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