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Self-training 1 2Self-training 3 4n What is Noisy Student? task. 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. [68, 24, 55, 22]. 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]. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. to noise the student. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. The comparison is shown in Table 9. This model investigates a new method. over the JFT dataset to predict a label for each image. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Our main results are shown in Table1. sign in This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. IEEE Transactions on Pattern Analysis and Machine Intelligence. 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. Med. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. The architectures for the student and teacher models can be the same or different. The performance drops when we further reduce it. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). 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. The paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task is revisited, and a simple recipe that is called Big Transfer (BiT) is created, which achieves strong performance on over 20 datasets. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. A tag already exists with the provided branch name. It is expensive and must be done with great care. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. 3.5B weakly labeled Instagram images. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. 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 This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. 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. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. We use the labeled images to train a teacher model using the standard cross entropy loss. 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. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. We iterate this process by putting back the student as the teacher. 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. Code is available at https://github.com/google-research/noisystudent. Summarization_self-training_with_noisy_student_improves_imagenet_classification. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. Ranked #14 on Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. Our procedure went as follows. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. 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 Their purpose is different from ours: to adapt a teacher model on one domain to another. We iterate this process by putting back the student as the teacher. Train a larger classifier on the combined set, adding noise (noisy student). . We then select images that have confidence of the label higher than 0.3. all 12, Image Classification Noise Self-training with Noisy Student 1. We duplicate images in classes where there are not enough images. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. There was a problem preparing your codespace, please try again. 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. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. 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]. With Noisy Student, the model correctly predicts dragonfly for the image. 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. 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. Use, Smithsonian In other words, small changes in the input image can cause large changes to the predictions. w Summary of key results compared to previous state-of-the-art models. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. We use stochastic depth[29], dropout[63] and RandAugment[14]. 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. Although they have produced promising results, in our preliminary experiments, consistency regularization works less well on ImageNet because consistency regularization in the early phase of ImageNet training regularizes the model towards high entropy predictions, and prevents it from achieving good accuracy. 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. Noisy Student Training is a semi-supervised learning approach. During this process, we kept increasing the size of the student model to improve the performance. 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. This is probably because it is harder to overfit the large unlabeled dataset. 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. 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 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. We sample 1.3M images in confidence intervals. 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 model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. The accuracy is improved by about 10% in most settings. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 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. We then train a larger EfficientNet as a student model on the We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. We use the standard augmentation instead of RandAugment in this experiment. 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. This invariance constraint reduces the degrees of freedom in the model. Notice, Smithsonian Terms of 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Our study shows that using unlabeled data improves accuracy and general robustness. 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]. ImageNet . 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. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. 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. EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. Noisy Student can still improve the accuracy to 1.6%. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. on ImageNet, which is 1.0 We determine number of training steps and the learning rate schedule by the batch size for labeled images. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. It implements SemiSupervised Learning with Noise to create an Image Classification. 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.