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

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Berglund, M. Honkala, H. Valpola, and T. Raiko, Semi-supervised learning with ladder networks, E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, Proceedings of the AAAI Conference on Artificial Intelligence, B. Recht, R. Roelofs, L. Schmidt, and V. Shankar. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. A tag already exists with the provided branch name. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Code for Noisy Student Training. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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). This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. 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. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. Self-training augmentation, dropout, stochastic depth to the student so that the noised We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. combination of labeled and pseudo labeled images. 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. 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. w Summary of key results compared to previous state-of-the-art models. We then train a larger EfficientNet as a student model on the Especially unlabeled images are plentiful and can be collected with ease. 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. The performance drops when we further reduce it. 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. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. 3.5B weakly labeled Instagram images. International Conference on Machine Learning, Learning extraction patterns for subjective expressions, Proceedings of the 2003 conference on Empirical methods in natural language processing, A. Roy Chowdhury, P. Chakrabarty, A. Singh, S. Jin, H. Jiang, L. Cao, and E. G. Learned-Miller, Automatic adaptation of object detectors to new domains using self-training, T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, Probability of error of some adaptive pattern-recognition machines, W. Shi, Y. Gong, C. Ding, Z. MaXiaoyu Tao, and N. Zheng, Transductive semi-supervised deep learning using min-max features, C. Simon-Gabriel, Y. Ollivier, L. Bottou, B. Schlkopf, and D. Lopez-Paz, First-order adversarial vulnerability of neural networks and input dimension, Very deep convolutional networks for large-scale image recognition, N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. Ranked #14 on Abdominal organ segmentation is very important for clinical applications. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). task. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Semi-supervised medical image classification with relation-driven self-ensembling model. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. Their purpose is different from ours: to adapt a teacher model on one domain to another. IEEE Trans. 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. The results also confirm that vision models can benefit from Noisy Student even without iterative training. . Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical 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. Here we study how to effectively use out-of-domain data. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. For more information about the large architectures, please refer to Table7 in Appendix A.1. 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. Use Git or checkout with SVN using the web URL. 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. 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. 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. 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. Hence we use soft pseudo labels for our experiments unless otherwise specified. 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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. Do better imagenet models transfer better? This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Edit social preview. We then use the teacher model to generate pseudo labels on unlabeled images. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. Note that these adversarial robustness results are not directly comparable to prior works since we use a large input resolution of 800x800 and adversarial vulnerability can scale with the input dimension[17, 20, 19, 61]. A number of studies, e.g. ImageNet-A top-1 accuracy from 16.6 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. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Code for Noisy Student Training. 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]. Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le Description: 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. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. We use the labeled images to train a teacher model using the standard cross entropy loss. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. 10687-10698 Abstract (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. self-mentoring outperforms data augmentation and self training. Parthasarathi et al. Soft pseudo labels lead to better performance for low confidence data. 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. unlabeled images. Unlike previous studies in semi-supervised learning that use in-domain unlabeled data (e.g, ., CIFAR-10 images as unlabeled data for a small CIFAR-10 training set), to improve ImageNet, we must use out-of-domain unlabeled data. Do imagenet classifiers generalize to imagenet? 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 improves imagenet classification. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. LeLinks:YouTube: https://www.youtube.com/c/yannickilcherTwitter: https://twitter.com/ykilcherDiscord: https://discord.gg/4H8xxDFBitChute: https://www.bitchute.com/channel/yannic-kilcherMinds: https://www.minds.com/ykilcherParler: https://parler.com/profile/YannicKilcherLinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/If you want to support me, the best thing to do is to share out the content :)If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):SubscribeStar (preferred to Patreon): https://www.subscribestar.com/yannickilcherPatreon: https://www.patreon.com/yannickilcherBitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cqEthereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9mMonero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. By showing the models only labeled images, we limit ourselves from making use of unlabeled images available in much larger quantities to improve accuracy and robustness of state-of-the-art models. 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. Computer Science - Computer Vision and Pattern Recognition. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. 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 use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Summarization_self-training_with_noisy_student_improves_imagenet_classification. On . 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. Train a classifier on labeled data (teacher). Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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. 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. . If nothing happens, download GitHub Desktop and try again. For classes where we have too many images, we take the images with the highest confidence. 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. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Work fast with our official CLI. In particular, we first perform normal training with a smaller resolution for 350 epochs. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. Different kinds of noise, however, may have different effects. sign in 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images et al. 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 model with Noisy Student can successfully predict the correct labels of these highly difficult images. Add a We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. We duplicate images in classes where there are not enough images. This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. Their noise model is video specific and not relevant for image 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. . These CVPR 2020 papers are the Open Access versions, provided by the. on ImageNet ReaL. Use, Smithsonian 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Our work is based on self-training (e.g.,[59, 79, 56]). As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 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 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. 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. 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. 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. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). The main use case of knowledge distillation is model compression by making the student model smaller. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative For example, with all noise removed, the accuracy drops from 84.9% to 84.3% in the case with 130M unlabeled images and drops from 83.9% to 83.2% in the case with 1.3M unlabeled images. On ImageNet, we first train an EfficientNet model on labeled images and use it as a teacher to generate pseudo labels for 300M unlabeled images. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. 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. 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. to use Codespaces. 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. 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 find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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.