self training with noisy student improves imagenet classification self training with noisy student improves imagenet classification

For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. 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. Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. 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. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). We iterate this process by putting back the student as the teacher. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . Our work is based on self-training (e.g.,[59, 79, 56]). 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. In other words, the student is forced to mimic a more powerful ensemble model. 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. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. on ImageNet ReaL [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. There was a problem preparing your codespace, please try again. On . Not only our method improves standard ImageNet accuracy, it also improves classification robustness on much harder test sets by large margins: ImageNet-A[25] top-1 accuracy from 16.6% to 74.2%, ImageNet-C[24] mean corruption error (mCE) from 45.7 to 31.2 and ImageNet-P[24] mean flip rate (mFR) from 27.8 to 16.1. Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. Their main goal is to find a small and fast model for deployment. Med. It is expensive and must be done with great care. On, International journal of molecular sciences. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. It implements SemiSupervised Learning with Noise to create an Image Classification. Code for Noisy Student Training. 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. Le. 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). Finally, frameworks in semi-supervised learning also include graph-based methods [84, 73, 77, 33], methods that make use of latent variables as target variables [32, 42, 78] and methods based on low-density separation[21, 58, 15], which might provide complementary benefits to our method. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. to noise the student. We then use the teacher model to generate pseudo labels on unlabeled images. 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. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. 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. We apply dropout to the final classification layer with a dropout rate of 0.5. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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. Then, that teacher is used to label the unlabeled data. - : self-training_with_noisy_student_improves_imagenet_classification 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. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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. Self-Training with Noisy Student Improves ImageNet Classification This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. A tag already exists with the provided branch name. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. to use Codespaces. But during the learning of the student, we inject noise such as data In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 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. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. 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. 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. 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. ImageNet-A top-1 accuracy from 16.6 Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. 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. We present a simple self-training method that achieves 87.4 The comparison is shown in Table 9. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. over the JFT dataset to predict a label for each image. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. 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 biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. Self-training 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. We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled images. Notice, Smithsonian Terms of Self-training with noisy student improves imagenet classification. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images 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. 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. On robustness test sets, it improves We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and , have shown that computer vision models lack robustness. 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. https://arxiv.org/abs/1911.04252. The algorithm is basically self-training, a method in semi-supervised learning (. You signed in with another tab or window. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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. If nothing happens, download Xcode and try again. Our study shows that using unlabeled data improves accuracy and general robustness. sign in 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 iterate this process by putting back the student as the teacher. 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. Do better imagenet models transfer better? You signed in with another tab or window. A common workaround is to use entropy minimization or ramp up the consistency loss. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Chowdhury et al. (using extra training data). Especially unlabeled images are plentiful and can be collected with ease. Noisy Student can still improve the accuracy to 1.6%. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. 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 iterate this process by putting back the student as the teacher. We then select images that have confidence of the label higher than 0.3. 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). Are you sure you want to create this branch? (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. As shown in Table2, Noisy Student with EfficientNet-L2 achieves 87.4% top-1 accuracy which is significantly better than the best previously reported accuracy on EfficientNet of 85.0%. The width. 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 architectures for the student and teacher models can be the same or different. 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. Noise Self-training with Noisy Student 1. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. In terms of methodology, putting back the student as the teacher. 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. With Noisy Student, the model correctly predicts dragonfly for the image. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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. For classes where we have too many images, we take the images with the highest confidence. For more information about the large architectures, please refer to Table7 in Appendix A.1. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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 main results are shown in Table1. 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. . Noisy Student Training seeks to improve on self-training and distillation in two ways. Different kinds of noise, however, may have different effects. 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. The inputs to the algorithm are both labeled and unlabeled images. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. all 12, Image Classification We then perform data filtering and balancing on this corpus. We also study the effects of using different amounts of unlabeled data. self-mentoring outperforms data augmentation and self training. We used the version from [47], which filtered the validation set of ImageNet. Self-Training With Noisy Student Improves ImageNet Classification. We also list EfficientNet-B7 as a reference. 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. First, we run an EfficientNet-B0 trained on ImageNet[69]. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . IEEE Transactions on Pattern Analysis and Machine Intelligence. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Copyright and all rights therein are retained by authors or by other copyright holders. In particular, we first perform normal training with a smaller resolution for 350 epochs. Learn more. 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. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. 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 train a larger EfficientNet as a student model on the 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. 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. Do imagenet classifiers generalize to imagenet? We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled 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. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Self-training 1 2Self-training 3 4n What is Noisy Student? We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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 with Noisy Student improves ImageNet classification. 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. The main use case of knowledge distillation is model compression by making the student model smaller. Astrophysical Observatory. We duplicate images in classes where there are not enough images. These CVPR 2020 papers are the Open Access versions, provided by the. labels, the teacher is not noised so that the pseudo labels are as good as 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 the following, we will first describe experiment details to achieve our results. . 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. on ImageNet ReaL. The most interesting image is shown on the right of the first row. 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. Please refer to [24] for details about mFR and AlexNets flip probability. (or is it just me), Smithsonian Privacy 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]. 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. Are labels required for improving adversarial robustness? Flip probability is the probability that the model changes top-1 prediction for different perturbations.