Code for Noisy Student Training. In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. 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. 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. Noisy Student can still improve the accuracy to 1.6%. Yalniz et al. Self-training with Noisy Student improves ImageNet classification. 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. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. We then use the teacher model to generate pseudo labels on unlabeled images. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. 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. Due to duplications, there are only 81M unique images among these 130M images. Self-training with Noisy Student improves ImageNet classification Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. ImageNet-A top-1 accuracy from 16.6 et al. The main difference between our method and knowledge distillation is that knowledge distillation does not consider unlabeled data and does not aim to improve the student model. It is expensive and must be done with great care. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. 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. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. There was a problem preparing your codespace, please try again. 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. For classes where we have too many images, we take the images with the highest confidence. 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. 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. For instance, on the right column, as the image of the car undergone a small rotation, the standard model changes its prediction from racing car to car wheel to fire engine. Self-training with Noisy Student improves ImageNet classification FixMatch-LS: Semi-supervised skin lesion classification with label We will then show our results on ImageNet and compare them with state-of-the-art models. This model investigates a new method. 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%. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. We duplicate images in classes where there are not enough images. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. ; 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. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. We iterate this process by putting back the student as the teacher. Please refer to [24] for details about mFR and AlexNets flip probability. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. On robustness test sets, it improves ImageNet-A top . tsai - Noisy student The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Iterative training is not used here for simplicity. 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. 2023.3.1_2 - Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. If nothing happens, download Xcode and try again. There was a problem preparing your codespace, please try again. Are you sure you want to create this branch? Notice, Smithsonian Terms of Self-training with noisy student improves imagenet classification. Summarization_self-training_with_noisy_student_improves_imagenet_classification. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. 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 main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. 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. 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. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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]. Our main results are shown in Table1. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. [^reference-9] [^reference-10] A critical insight was to . 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]. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. to noise the student. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Self-Training for Natural Language Understanding! SelfSelf-training with Noisy Student improves ImageNet classification 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. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin 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. 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. 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 use the same architecture for the teacher and the student and do not perform iterative training. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Learn more. 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. 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. Semi-supervised medical image classification with relation-driven self-ensembling model. Noisy Student Explained | Papers With Code 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. 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. We do not tune these hyperparameters extensively since our method is highly robust to them. 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. 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. Self-Training Noisy Student " " Self-Training . This invariance constraint reduces the degrees of freedom in the model. 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. 27.8 to 16.1. Noisy Student Training is a semi-supervised learning approach. A common workaround is to use entropy minimization or ramp up the consistency loss. We sample 1.3M images in confidence intervals. to use Codespaces. 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. 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). 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. Distillation Survey : Noisy Student | 9to5Tutorial For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. Further, Noisy Student outperforms the state-of-the-art accuracy of 86.4% by FixRes ResNeXt-101 WSL[44, 71] that requires 3.5 Billion Instagram images labeled with tags. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. For each class, we select at most 130K images that have the highest confidence. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. On, International journal of molecular sciences. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Please refer to [24] for details about mCE and AlexNets error rate. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. Different kinds of noise, however, may have different effects. Finally, frameworks in semi-supervised learning also include graph-based methods [84, 73, 77, 33], methods that make use of latent variables as target variables [32, 42, 78] and methods based on low-density separation[21, 58, 15], which might provide complementary benefits to our method. ImageNet . Self-training with Noisy Student - task. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical IEEE Trans. A. Krizhevsky, I. Sutskever, and G. E. Hinton, Temporal ensembling for semi-supervised learning, Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks, Workshop on Challenges in Representation Learning, ICML, Certainty-driven consistency loss for semi-supervised learning, C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, R. G. Lopes, D. Yin, B. Poole, J. Gilmer, and E. D. Cubuk, Improving robustness without sacrificing accuracy with patch gaussian augmentation, Y. Luo, J. Zhu, M. Li, Y. Ren, and B. Zhang, Smooth neighbors on teacher graphs for semi-supervised learning, L. Maale, C. K. Snderby, S. K. Snderby, and O. Winther, A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, Towards deep learning models resistant to adversarial attacks, D. Mahajan, R. Girshick, V. Ramanathan, K. He, M. Paluri, Y. Li, A. Bharambe, and L. van der Maaten, Exploring the limits of weakly supervised pretraining, T. Miyato, S. Maeda, S. Ishii, and M. Koyama, Virtual adversarial training: a regularization method for supervised and semi-supervised learning, IEEE transactions on pattern analysis and machine intelligence, A. Najafi, S. Maeda, M. Koyama, and T. Miyato, Robustness to adversarial perturbations in learning from incomplete data, J. Ngiam, D. Peng, V. Vasudevan, S. Kornblith, Q. V. Le, and R. Pang, Robustness properties of facebooks resnext wsl models, Adversarial dropout for supervised and semi-supervised learning, Lessons from building acoustic models with a million hours of speech, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), S. Qiao, W. Shen, Z. Zhang, B. Wang, and A. Yuille, Deep co-training for semi-supervised image recognition, I. Radosavovic, P. Dollr, R. Girshick, G. Gkioxari, and K. He, Data distillation: towards omni-supervised learning, A. Rasmus, M. 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. 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. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Finally, in the above, we say that the pseudo labels can be soft or hard. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Self-Training With Noisy Student Improves ImageNet Classification This work proposes a novel architectural unit, which is term the Squeeze-and-Excitation (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and shows that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. 3.5B weakly labeled Instagram images. The baseline model achieves an accuracy of 83.2. Self-training with Noisy Student - Medium 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. w Summary of key results compared to previous state-of-the-art models. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. 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. 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].

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