In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. 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. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. There was a problem preparing your codespace, please try again. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. 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 score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. Our work is based on self-training (e.g.,[59, 79, 56]). We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Add a Hence the total number of images that we use for training a student model is 130M (with some duplicated images). In other words, the student is forced to mimic a more powerful ensemble model. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. 3.5B weakly labeled Instagram images. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. et al. We use stochastic depth[29], dropout[63] and RandAugment[14]. This model investigates a new method. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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. 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. Abdominal organ segmentation is very important for clinical applications. Work fast with our official CLI. 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. Image Classification Copyright and all rights therein are retained by authors or by other copyright holders. As noise injection methods are not used in the student model, and the student model was also small, it is more difficult to make the student better than teacher. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. Are you sure you want to create this branch? Self-Training with Noisy Student Improves ImageNet Classification student is forced to learn harder from the pseudo labels. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Use, Smithsonian 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. The main use case of knowledge distillation is model compression by making the student model smaller. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. During this process, we kept increasing the size of the student model to improve the performance. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. A tag already exists with the provided branch name. Use Git or checkout with SVN using the web URL. . This invariance constraint reduces the degrees of freedom in the model. At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. 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. We use EfficientNet-B0 as both the teacher model and the student model and compare using Noisy Student with soft pseudo labels and hard pseudo labels. Their noise model is video specific and not relevant for image classification. 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. 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. 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. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Please 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. 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. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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 then select images that have confidence of the label higher than 0.3. We also study the effects of using different amounts of unlabeled data. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. and surprising gains on robustness and adversarial benchmarks. Noisy Student (B7, L2) means to use EfficientNet-B7 as the student and use our best model with 87.4% accuracy as the teacher model. over the JFT dataset to predict a label for each image. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. Self-training with Noisy Student improves ImageNet classification Abstract. 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. Self-training with Noisy Student improves ImageNet classification. on ImageNet ReaL A common workaround is to use entropy minimization or ramp up the consistency loss. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. . We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. Parthasarathi et al. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Are labels required for improving adversarial robustness? We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We use the standard augmentation instead of RandAugment in this experiment. supervised model from 97.9% accuracy to 98.6% accuracy. Please refer to [24] for details about mFR and AlexNets flip probability. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. These significant gains in robustness in ImageNet-C and ImageNet-P are surprising because our models were not deliberately optimizing for robustness (e.g., via data augmentation). Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. For each class, we select at most 130K images that have the highest confidence. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Although noise may appear to be limited and uninteresting, when it is applied to unlabeled data, it has a compound benefit of enforcing local smoothness in the decision function on both labeled and unlabeled data. This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. (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. 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. Different kinds of noise, however, may have different effects. 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. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. In terms of methodology, 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. 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. 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). For more information about the large architectures, please refer to Table7 in Appendix A.1. 10687-10698 Abstract Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. 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. We start with the 130M unlabeled images and gradually reduce the number of images. Noisy Student leads to significant improvements across all model sizes for EfficientNet. We iterate this process by putting back the student as the teacher. 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. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. As can be seen from the figure, our model with Noisy Student makes correct predictions for images under severe corruptions and perturbations such as snow, motion blur and fog, while the model without Noisy Student suffers greatly under these conditions. Train a classifier on labeled data (teacher). It is expensive and must be done with great care. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Flip probability is the probability that the model changes top-1 prediction for different perturbations. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Summarization_self-training_with_noisy_student_improves_imagenet_classification. to use Codespaces. Do better imagenet models transfer better? This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. If nothing happens, download GitHub Desktop and try again. 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. 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. We iterate this process by putting back the student as the teacher. 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. ; 2006)[book reviews], Semi-supervised deep learning with memory, Proceedings of the European Conference on Computer Vision (ECCV), Xception: deep learning with depthwise separable convolutions, K. Clark, M. Luong, C. D. Manning, and Q. V. Le, Semi-supervised sequence modeling with cross-view training, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, AutoAugment: learning augmentation strategies from data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, RandAugment: practical data augmentation with no separate search, Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, Good semi-supervised learning that requires a bad gan, T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, A. Galloway, A. Golubeva, T. Tanay, M. Moussa, and G. W. Taylor, R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow, I. J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and harnessing adversarial examples, Semi-supervised learning by entropy minimization, Advances in neural information processing systems, K. Gu, B. Yang, J. Ngiam, Q. Are you sure you want to create this branch? Our procedure went as follows. 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. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from We iterate this process by putting back the student as the teacher. 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 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. augmentation, dropout, stochastic depth to the student so that the noised We duplicate images in classes where there are not enough images. First, we run an EfficientNet-B0 trained on ImageNet[69]. Finally, in the above, we say that the pseudo labels can be soft or hard. We also list EfficientNet-B7 as a reference. 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. [^reference-9] [^reference-10] A critical insight was to . [57] used self-training for domain adaptation. 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.