We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Noisy Student leads to significant improvements across all model sizes for EfficientNet. Due to duplications, there are only 81M unique images among these 130M images. 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. Self-Training With Noisy Student Improves ImageNet Classification. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. We then select images that have confidence of the label higher than 0.3. 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. (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. 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. We then train a larger EfficientNet as a student model on the Le. During the generation of the pseudo 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. If nothing happens, download Xcode and try again. Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. 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. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. Self-training with Noisy Student improves ImageNet classificationCVPR2020, Codehttps://github.com/google-research/noisystudent, Self-training, 1, 2Self-training, Self-trainingGoogleNoisy Student, Noisy Studentstudent modeldropout, stochastic depth andaugmentationteacher modelNoisy Noisy Student, Noisy Student, 1, JFT3ImageNetEfficientNet-B00.3130K130K, EfficientNetbaseline modelsEfficientNetresnet, EfficientNet-B7EfficientNet-L0L1L2, batchsize = 2048 51210242048EfficientNet-B4EfficientNet-L0l1L2350epoch700epoch, 2EfficientNet-B7EfficientNet-L0, 3EfficientNet-L0EfficientNet-L1L0, 4EfficientNet-L1EfficientNet-L2, student modelNoisy, noisystudent modelteacher modelNoisy, Noisy, Self-trainingaugmentationdropoutstochastic depth, Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores., 12/self-training-with-noisy-student-f33640edbab2, EfficientNet-L0EfficientNet-B7B7, EfficientNet-L1EfficientNet-L0, EfficientNetsEfficientNet-L1EfficientNet-L2EfficientNet-L2EfficientNet-B75. 3429-3440. . The algorithm is basically self-training, a method in semi-supervised learning (. We find that Noisy Student is better with an additional trick: data balancing. unlabeled images , . Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. With Noisy Student, the model correctly predicts dragonfly for the image. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Noisy Student can still improve the accuracy to 1.6%. task. The comparison is shown in Table 9. Please refer to [24] for details about mFR and AlexNets flip probability. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. 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. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. On . 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. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. You signed in with another tab or window. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. [57] used self-training for domain adaptation. In the following, we will first describe experiment details to achieve our results. Notice, Smithsonian Terms of As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Ranked #14 on 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. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Code for Noisy Student Training. The most interesting image is shown on the right of the first row. 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. Please In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. 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. Different kinds of noise, however, may have different effects. Self-training 1 2Self-training 3 4n What is Noisy Student? We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. 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. Train a larger classifier on the combined set, adding noise (noisy student). 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. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. Train a larger classifier on the combined set, adding noise (noisy student). sign in 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. Self-training https://arxiv.org/abs/1911.04252. 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. . Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. 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. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. [^reference-9] [^reference-10] A critical insight was to . [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . We use a resolution of 800x800 in this experiment. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a Code is available at https://github.com/google-research/noisystudent. Train a classifier on labeled data (teacher). Astrophysical Observatory. 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. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). on ImageNet, which is 1.0 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. Self-training with Noisy Student improves ImageNet classification. This invariance constraint reduces the degrees of freedom in the model. 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. Are you sure you want to create this branch? ; 2006)[book reviews], Semi-supervised deep learning with memory, Proceedings of the European Conference on Computer Vision (ECCV), Xception: deep learning with depthwise separable convolutions, K. Clark, M. Luong, C. D. Manning, and Q. V. Le, Semi-supervised sequence modeling with cross-view training, E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, AutoAugment: learning augmentation strategies from data, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, RandAugment: practical data augmentation with no separate search, Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov, Good semi-supervised learning that requires a bad gan, T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, A. Galloway, A. Golubeva, T. Tanay, M. Moussa, and G. W. Taylor, R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel, ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness, J. Gilmer, L. Metz, F. Faghri, S. S. Schoenholz, M. Raghu, M. Wattenberg, and I. Goodfellow, I. J. Goodfellow, J. Shlens, and C. Szegedy, Explaining and harnessing adversarial examples, Semi-supervised learning by entropy minimization, Advances in neural information processing systems, K. Gu, B. Yang, J. Ngiam, Q. 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. 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. 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%. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. Are you sure you want to create this branch? 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. 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. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. On robustness test sets, it improves 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. and surprising gains on robustness and adversarial benchmarks. Their noise model is video specific and not relevant for image classification. to use Codespaces. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. We iterate this process by putting back the student as the teacher. 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. 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. As can be seen from Table 8, the performance stays similar when we reduce the data to 116 of the total data, which amounts to 8.1M images after duplicating. 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. For labeled images, we use a batch size of 2048 by default and reduce the batch size when we could not fit the model into the memory. 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. 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. . In contrast, the predictions of the model with Noisy Student remain quite stable. Chowdhury et al. . 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. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. Are labels required for improving adversarial robustness? supervised model from 97.9% accuracy to 98.6% accuracy. 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 model investigates a new method. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Our main results are shown in Table1. We use the labeled images to train a teacher model using the standard cross entropy loss. Noisy Student Training seeks to improve on self-training and distillation in two ways. Use Git or checkout with SVN using the web URL. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. The inputs to the algorithm are both labeled and unlabeled images. Computer Science - Computer Vision and Pattern Recognition. 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. 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. 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. Hence we use soft pseudo labels for our experiments unless otherwise specified. Image Classification We improved it by adding noise to the student to learn beyond the teachers knowledge. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. IEEE Transactions on Pattern Analysis and Machine Intelligence. These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. We used the version from [47], which filtered the validation set of ImageNet. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. . Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Imaging, 39 (11) (2020), pp. ImageNet . Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). 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. The ONCE (One millioN sCenEs) dataset for 3D object detection in the autonomous driving scenario is introduced and a benchmark is provided in which a variety of self-supervised and semi- supervised methods on the ONCE dataset are evaluated. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. 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 mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. Self-training with Noisy Student improves ImageNet classification. 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.
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