Distillation Survey : Noisy Student | 9to5Tutorial In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Then, that teacher is used to label the unlabeled data. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. This model investigates a new method. 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 Figure 1(c) shows images from ImageNet-P and the corresponding predictions. Self-Training With Noisy Student Improves ImageNet Classification We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. GitHub - google-research/noisystudent: Code for Noisy Student Training Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Self-training with Noisy Student improves ImageNet classification (or is it just me), Smithsonian Privacy Use Git or checkout with SVN using the web URL. 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. Our main results are shown in Table1. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . 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 is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). [57] used self-training for domain adaptation. 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, qvlg@google.com, hovy@cmu.edu Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . 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. We use stochastic depth[29], dropout[63] and RandAugment[14]. 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. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to 83.7%, reduces ImageNet-C mean corruption error from 45.7 to 28.3, and reduces ImageNet-P mean flip rate from 27.8 to 12.2. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. ImageNet images and use it as a teacher to generate pseudo labels on 300M Code for Noisy Student Training. . 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. 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. putting back the student as the teacher. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. [68, 24, 55, 22]. 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). Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. 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. 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. These CVPR 2020 papers are the Open Access versions, provided by the. In contrast, the predictions of the model with Noisy Student remain quite stable. , have shown that computer vision models lack robustness. augmentation, dropout, stochastic depth to the student so that the noised 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 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. 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. In terms of methodology, On, International journal of molecular sciences. 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. unlabeled images , . Learn more. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Chowdhury et al. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. 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). Agreement NNX16AC86A, Is ADS down? 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. 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. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. A. Alemi, Thirty-First AAAI Conference on Artificial Intelligence, C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, EfficientNet: rethinking model scaling for convolutional neural networks, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, A. Vedaldi, M. Douze, and H. Jgou, Fixing the train-test resolution discrepancy, V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), J. Weston, F. Ratle, H. Mobahi, and R. Collobert, Deep learning via semi-supervised embedding, Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le, Unsupervised data augmentation for consistency training, S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, I. You signed in with another tab or window. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. 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. SelfSelf-training with Noisy Student improves ImageNet classification 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 found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. The main use case of knowledge distillation is model compression by making the student model smaller. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. Self-Training for Natural Language Understanding! Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. We do not tune these hyperparameters extensively since our method is highly robust to them. 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. For classes where we have too many images, we take the images with the highest confidence. 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. A tag already exists with the provided branch name. . 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. 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. Self-training with Noisy Student. Ranked #14 on The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Our work is based on self-training (e.g.,[59, 79, 56]). We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. We iterate this process by putting back the student as the teacher. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. to use Codespaces. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. 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. . We then train a larger EfficientNet as a student model on the The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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. The abundance of data on the internet is vast. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. In particular, we first perform normal training with a smaller resolution for 350 epochs. 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. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Self-Training With Noisy Student Improves ImageNet Classification Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. We iterate this process by putting back the student as the teacher. on ImageNet ReaL 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. Self-training was previously used to improve ResNet-50 from 76.4% to 81.2% top-1 accuracy[76] which is still far from the state-of-the-art accuracy. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. Please Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. But training robust supervised learning models is requires this step. 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. Le. 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. 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. Soft pseudo labels lead to better performance for low confidence data. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. 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]. Noisy Student Explained | Papers With Code Since we use soft pseudo labels generated from the teacher model, when the student is trained to be exactly the same as the teacher model, the cross entropy loss on unlabeled data would be zero and the training signal would vanish. Please refer to [24] for details about mFR and AlexNets flip probability. sign in Figure 1(a) shows example images from ImageNet-A and the predictions of our models. 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]. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Self-training with Noisy Student improves ImageNet classification Since a teacher models confidence on an image can be a good indicator of whether it is an out-of-domain image, we consider the high-confidence images as in-domain images and the low-confidence images as out-of-domain images. Self-training with Noisy Student improves ImageNet classification Abstract. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Self-Training Noisy Student " " Self-Training . 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. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled 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. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards Flip probability is the probability that the model changes top-1 prediction for different perturbations. 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Self-training with Noisy Student improves ImageNet classification Med. We used the version from [47], which filtered the validation set of ImageNet. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. The algorithm is basically self-training, a method in semi-supervised learning (. labels, the teacher is not noised so that the pseudo labels are as good as As shown in Table3,4 and5, when compared with the previous state-of-the-art model ResNeXt-101 WSL[44, 48] trained on 3.5B weakly labeled images, Noisy Student yields substantial gains on robustness datasets. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. Infer labels on a much larger unlabeled dataset. We also list EfficientNet-B7 as a reference. Please and surprising gains on robustness and adversarial benchmarks. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and Hence the total number of images that we use for training a student model is 130M (with some duplicated images). Code is available at https://github.com/google-research/noisystudent. We iterate this process by putting back the student as the teacher. Self-training with Noisy Student improves ImageNet classification 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). all 12, Image Classification 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 Are you sure you want to create this branch? Self-Training With Noisy Student Improves ImageNet Classification 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. 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. 27.8 to 16.1. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. On . Noisy Student can still improve the accuracy to 1.6%. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. Self-training with Noisy Student improves ImageNet classification 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline.

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