IEEE Trans. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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 find that Noisy Student is better with an additional trick: data balancing. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. 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 . First, a teacher model is trained in a supervised fashion. 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. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. 3.5B weakly labeled Instagram images. to use Codespaces. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Computer Science - Computer Vision and Pattern Recognition. Self-training with Noisy Student improves ImageNet classification The performance consistently drops with noise function removed. The mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. Iterative training is not used here for simplicity. Noisy Student Training is a semi-supervised learning approach. 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]). student is forced to learn harder from the pseudo labels. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. We start with the 130M unlabeled images and gradually reduce the number of images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. Use Git or checkout with SVN using the web URL. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. 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. 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. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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. It is expensive and must be done with great care. 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. PDF Self-Training with Noisy Student Improves ImageNet Classification We also study the effects of using different amounts of unlabeled data. 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. We use the same architecture for the teacher and the student and do not perform iterative training. Self-training with Noisy Student improves ImageNet classification Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. Zoph et al. The accuracy is improved by about 10% in most settings. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. 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. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. 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. 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. 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. et al. Noisy StudentImageNetEfficientNet-L2state-of-the-art. 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]. Train a classifier on labeled data (teacher). . Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Train a larger classifier on the combined set, adding noise (noisy student). In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. Code for Noisy Student Training. - : self-training_with_noisy_student_improves_imagenet_classification over the JFT dataset to predict a label for each image. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). 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. augmentation, dropout, stochastic depth to the student so that the noised The main use case of knowledge distillation is model compression by making the student model smaller. IEEE Transactions on Pattern Analysis and Machine Intelligence. 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 is a semi-supervised learning method which achieves 88.4% top-1 accuracy on ImageNet (SOTA) and surprising gains on robustness and adversarial benchmarks. w Summary of key results compared to previous state-of-the-art models. 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. 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. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Yalniz et al. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model 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. The architectures for the student and teacher models can be the same or different. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. 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. possible. If nothing happens, download Xcode and try again. Self-training with Noisy Student improves ImageNet classification. If nothing happens, download GitHub Desktop and try again. self-mentoring outperforms data augmentation and self training. 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. For classes where we have too many images, we take the images with the highest confidence. 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. 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. Self-Training With Noisy Student Improves ImageNet Classification Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. 2023.3.1_2 - Do better imagenet models transfer better? This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. A tag already exists with the provided branch name. This is probably because it is harder to overfit the large unlabeled dataset. Add a First, we run an EfficientNet-B0 trained on ImageNet[69]. Significantly, after using the masks generated by student-SN, the classification performance improved by 0.9 of AC, 0.7 of SE, and 0.9 of AUC. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. 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. EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . There was a problem preparing your codespace, please try again. unlabeled images , . Self-mentoring: : A new deep learning pipeline to train a self 27.8 to 16.1. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. 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]. ImageNet . 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. We use stochastic depth[29], dropout[63] and RandAugment[14]. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin If you get a better model, you can use the model to predict pseudo-labels on the filtered data. 10687-10698). Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. Similar to[71], we fix the shallow layers during finetuning. 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. Flip probability is the probability that the model changes top-1 prediction for different perturbations. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). 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. Please Do imagenet classifiers generalize to imagenet? 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. Self-Training With Noisy Student Improves ImageNet Classification 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. Self-training 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. 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. The most interesting image is shown on the right of the first row. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . CVPR 2020 Open Access Repository Why Self-training with Noisy Students beats SOTA Image classification With Noisy Student, the model correctly predicts dragonfly for the image. 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. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Abdominal organ segmentation is very important for clinical applications. Use Git or checkout with SVN using the web URL. 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 method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. ImageNet images and use it as a teacher to generate pseudo labels on 300M Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 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. 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. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. 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. 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. Are you sure you want to create this branch? Different kinds of noise, however, may have different effects. If nothing happens, download Xcode and try again. But during the learning of the student, we inject noise such as data 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 We duplicate images in classes where there are not enough images. Self-training with Noisy Student improves ImageNet classification. 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. We used the version from [47], which filtered the validation set of ImageNet. To achieve this result, we first train an EfficientNet model on labeled During this process, we kept increasing the size of the student model to improve the performance. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . 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.