However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. on ImageNet ReaL Agreement NNX16AC86A, Is ADS down? In this section, we study the importance of noise and the effect of several noise methods used in our model. Are you sure you want to create this branch? This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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. But during the learning of the student, we inject noise such as data Finally, in the above, we say that the pseudo labels can be soft or hard. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. We use the standard augmentation instead of RandAugment in this experiment. 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. task. 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. 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. 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. 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. Yalniz et al. (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. https://arxiv.org/abs/1911.04252, Accompanying notebook and sources to "A Guide to Pseudolabelling: How to get a Kaggle medal with only one model" (Dec. 2020 PyData Boston-Cambridge Keynote), Deep learning has shown remarkable successes in image recognition in recent years[35, 66, 62, 23, 69]. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Noise Self-training with Noisy Student 1. We evaluate the best model, that achieves 87.4% top-1 accuracy, on three robustness test sets: ImageNet-A, ImageNet-C and ImageNet-P. ImageNet-C and P test sets[24] include images with common corruptions and perturbations such as blurring, fogging, rotation and scaling. 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. 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. Self-training with Noisy Student improves ImageNet classification. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. We use stochastic depth[29], dropout[63] and RandAugment[14]. Computer Science - Computer Vision and Pattern Recognition. During this process, we kept increasing the size of the student model to improve the performance. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. self-mentoring outperforms data augmentation and 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. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. 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. . 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. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (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. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. Do imagenet classifiers generalize to imagenet? After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 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. Our procedure went as follows. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The results also confirm that vision models can benefit from Noisy Student even without iterative training. Are you sure you want to create this branch? In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. Astrophysical Observatory. Our main results are shown in Table1. 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 thank the Google Brain team, Zihang Dai, Jeff Dean, Hieu Pham, Colin Raffel, Ilya Sutskever and Mingxing Tan for insightful discussions, Cihang Xie for robustness evaluation, Guokun Lai, Jiquan Ngiam, Jiateng Xie and Adams Wei Yu for feedbacks on the draft, Yanping Huang and Sameer Kumar for improving TPU implementation, Ekin Dogus Cubuk and Barret Zoph for help with RandAugment, Yanan Bao, Zheyun Feng and Daiyi Peng for help with the JFT dataset, Olga Wichrowska and Ola Spyra for help with infrastructure. , have shown that computer vision models lack robustness. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. 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. ; 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. over the JFT dataset to predict a label for each image. Similar to[71], we fix the shallow layers during finetuning. Semi-supervised medical image classification with relation-driven self-ensembling model. labels, the teacher is not noised so that the pseudo labels are as good as 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). We present a simple self-training method that achieves 87.4 [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Iterative training is not used here for simplicity. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative 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. Their noise model is video specific and not relevant for image classification. et al. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. 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. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. 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. 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. This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. 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 . If nothing happens, download Xcode and try again. IEEE Trans. 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. 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. We iterate this process by 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 method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. 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. Notice, Smithsonian Terms of Code for Noisy Student Training. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. - : self-training_with_noisy_student_improves_imagenet_classification 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. Self-training 1 2Self-training 3 4n What is Noisy Student? Work fast with our official CLI. 27.8 to 16.1. 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. 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. 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. Train a classifier on labeled data (teacher). We duplicate images in classes where there are not enough images. 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. Abdominal organ segmentation is very important for clinical applications. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Noisy Student can still improve the accuracy to 1.6%. team using this approach not only surpasses the top-1 ImageNet accuracy of SOTA models by 1%, it also shows that the robustness of a model also improves. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. But training robust supervised learning models is requires this step. 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]. Flip probability is the probability that the model changes top-1 prediction for different perturbations. 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. to noise the student. 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. These CVPR 2020 papers are the Open Access versions, provided by the. 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. Models are available at this https URL. all 12, Image Classification Train a larger classifier on the combined set, adding noise (noisy student). You signed in with another tab or window. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. It 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. Are labels required for improving adversarial robustness? Their main goal is to find a small and fast model for deployment. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images (using extra training data). We also list EfficientNet-B7 as a reference. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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 do not tune these hyperparameters extensively since our method is highly robust to them. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. . Especially unlabeled images are plentiful and can be collected with ease. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. There was a problem preparing your codespace, please try again. Use, Smithsonian We also study the effects of using different amounts of 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. . As can be seen from Table 8, the performance stays similar when we reduce the data to 116 of the total data, which amounts to 8.1M images after duplicating. For unlabeled images, we set the batch size to be three times the batch size of labeled images for large models, including EfficientNet-B7, L0, L1 and L2. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. To achieve this result, we first train an EfficientNet model on labeled 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. 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. For more information about the large architectures, please refer to Table7 in Appendix A.1. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Self-Training With Noisy Student Improves ImageNet Classification. 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. 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]. sign in 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. You signed in with another tab or window. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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.