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. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. If nothing happens, download Xcode and try again. Noisy Student self-training is an effective way to leverage unlabelled datasets and improving accuracy by adding noise to the student model while training so it learns beyond the teacher's knowledge. 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. See 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. Their main goal is to find a small and fast model for deployment. 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. Astrophysical Observatory. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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. Especially unlabeled images are plentiful and can be collected with ease. We apply dropout to the final classification layer with a dropout rate of 0.5. We then perform data filtering and balancing on this corpus. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. combination of labeled and pseudo labeled images. and surprising gains on robustness and adversarial benchmarks. (or is it just me), Smithsonian Privacy Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. Are you sure you want to create this branch? In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. 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. 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. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. We use the standard augmentation instead of RandAugment in this experiment. Noisy Students performance improves with more unlabeled data. (using extra training data). Due to duplications, there are only 81M unique images among these 130M images. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. 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]. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Self-training with Noisy Student improves ImageNet classification Abstract. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. We use EfficientNet-B4 as both the teacher and the student. 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). 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. Noise Self-training with Noisy Student 1. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. w Summary of key results compared to previous state-of-the-art models. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. 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. . We iterate this process by putting back the student as the teacher. Use Git or checkout with SVN using the web URL. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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. 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. On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. To achieve this result, we first train an EfficientNet model on labeled Hence the total number of images that we use for training a student model is 130M (with some duplicated images). [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. 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. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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. 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]). 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. 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. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. The paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task is revisited, and a simple recipe that is called Big Transfer (BiT) is created, which achieves strong performance on over 20 datasets. In contrast, the predictions of the model with Noisy Student remain quite stable. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. 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. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). This model investigates a new method. This is probably because it is harder to overfit the large unlabeled dataset. Self-training is a form of semi-supervised learning [10] which attempts to leverage unlabeled data to improve classification performance in the limited data regime. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. https://arxiv.org/abs/1911.04252. 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. On . Code is available at https://github.com/google-research/noisystudent. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. We will then show our results on ImageNet and compare them with state-of-the-art models. 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 use the same architecture for the teacher and the student and do not perform iterative training. 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, [email protected], [email protected] Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . We start with the 130M unlabeled images and gradually reduce the number of images. sign in In this section, we study the importance of noise and the effect of several noise methods used in our model. 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. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. The abundance of data on the internet is vast. 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. 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. 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. But training robust supervised learning models is requires this step. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. We use a resolution of 800x800 in this experiment. Their noise model is video specific and not relevant for image classification. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. Different kinds of noise, however, may have different effects. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10687-10698, (2020 . We then select images that have confidence of the label higher than 0.3. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. It is experimentally validated that, for a target test resolution, using a lower train resolution offers better classification at test time, and a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ is proposed. Self-training 1 2Self-training 3 4n What is Noisy Student? Self-training with Noisy Student. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet Semi-supervised medical image classification with relation-driven self-ensembling model. Imaging, 39 (11) (2020), pp. 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. Train a classifier on labeled data (teacher). We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Le. 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 iterate this process by putting back the student as the teacher. This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. 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. Agreement NNX16AC86A, Is ADS down? 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. Self-training with noisy student improves imagenet classification. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger 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. IEEE Transactions on Pattern Analysis and Machine Intelligence. Hence we use soft pseudo labels for our experiments unless otherwise specified. 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. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. In particular, we first perform normal training with a smaller resolution for 350 epochs. 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. After using the masks generated by teacher-SN, the classification performance improved by 0.2 of AC, 1.2 of SP, and 0.7 of AUC. Code for Noisy Student Training. ImageNet-A top-1 accuracy from 16.6 3.5B weakly labeled Instagram images. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y. A tag already exists with the provided branch name. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . 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. 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). We also list EfficientNet-B7 as a reference. The best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative 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. 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 work is based on self-training (e.g.,[59, 79, 56]). Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. There was a problem preparing your codespace, please try again. You signed in with another tab or window. student is forced to learn harder from the pseudo labels. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. Please 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. 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. We use stochastic depth[29], dropout[63] and RandAugment[14]. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. on ImageNet ReaL Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. E. Arazo, D. Ortego, P. Albert, N. E. OConnor, and K. McGuinness, Pseudo-labeling and confirmation bias in deep semi-supervised learning, B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson, There are many consistent explanations of unlabeled data: why you should average, International Conference on Learning Representations, Advances in Neural Information Processing Systems, D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, MixMatch: a holistic approach to semi-supervised learning, Combining labeled and unlabeled data with co-training, C. Bucilu, R. Caruana, and A. Niculescu-Mizil, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Y. Carmon, A. Raghunathan, L. Schmidt, P. Liang, and J. C. Duchi, Unlabeled data improves adversarial robustness, Semi-supervised learning (chapelle, o. et al., eds. Ranked #14 on We use the labeled images to train a teacher model using the standard cross entropy loss. In other words, the student is forced to mimic a more powerful ensemble model. It can be seen that masks are useful in improving classification performance. 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]. You signed in with another tab or window. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Here we study how to effectively use out-of-domain data. 3429-3440. . Use Git or checkout with SVN using the web URL. 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. 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. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. However, in the case with 130M unlabeled images, with noise function removed, the performance is still improved to 84.3% from 84.0% when compared to the supervised baseline. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 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. 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. We also study the effects of using different amounts of unlabeled data. 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. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). . 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. We iterate this process by putting back the student as the teacher. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. 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 robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Computer Science - Computer Vision and Pattern Recognition. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Figure 1(b) shows images from ImageNet-C and the corresponding predictions.