On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. 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. 10687-10698). Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. 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 possible. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Learn more. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. combination of labeled and pseudo labeled images. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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. Yalniz et al. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. In terms of methodology, task. Our work is based on self-training (e.g.,[59, 79, 56]). This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. 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. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. If nothing happens, download GitHub Desktop and try again. 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. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Similar to[71], we fix the shallow layers during finetuning. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. 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. 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]. et al. 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. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. putting back the student as the teacher. This invariance constraint reduces the degrees of freedom in the model. 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. Le. 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]. We iterate this process by putting back the student as the teacher. For RandAugment, we apply two random operations with the magnitude set to 27. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. The abundance of data on the internet is vast. To intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct. Noisy Student leads to significant improvements across all model sizes for EfficientNet. 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. Use Git or checkout with SVN using the web URL. When data augmentation noise is used, the student must ensure that a translated image, for example, should have the same category with a non-translated image. Imaging, 39 (11) (2020), pp. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). augmentation, dropout, stochastic depth to the student so that the noised 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. 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. 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. 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. Our experiments show that an important element for this simple method to work well at scale is that the student model should be noised during its training while the teacher should not be noised during the generation of pseudo labels. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. We use a resolution of 800x800 in this experiment. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . During the generation of the pseudo You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. The algorithm is basically self-training, a method in semi-supervised learning (. 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. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. Computer Science - Computer Vision and Pattern Recognition. A tag already exists with the provided branch name. Edit social preview. We use stochastic depth[29], dropout[63] and RandAugment[14]. 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. all 12, Image Classification For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. It can be seen that masks are useful in improving classification performance. ImageNet . 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 first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. 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. over the JFT dataset to predict a label for each image. Test images on ImageNet-P underwent different scales of perturbations. 10687-10698 Abstract As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. We then select images that have confidence of the label higher than 0.3. Our study shows that using unlabeled data improves accuracy and general robustness. 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. A number of studies, e.g. We iterate this process by 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. Code is available at https://github.com/google-research/noisystudent. In particular, we first perform normal training with a smaller resolution for 350 epochs. Image Classification 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. Then, that teacher is used to label the unlabeled data. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a EfficientNet with Noisy Student produces correct top-1 predictions (shown in. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Do better imagenet models transfer better? The proposed use of distillation to only handle easy instances allows for a more aggressive trade-off in the student size, thereby reducing the amortized cost of inference and achieving better accuracy than standard distillation. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. See In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Chowdhury et al. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. Train a larger classifier on the combined set, adding noise (noisy student). 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. 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. 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. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. For example, without Noisy Student, the model predicts bullfrog for the image shown on the left of the second row, which might be resulted from the black lotus leaf on the water. 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.

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