self training with noisy student improves imagenet classification

IEEE Transactions on Pattern Analysis and Machine Intelligence. CVPR 2020 Open Access Repository Noise Self-training with Noisy Student 1. all 12, Image Classification We then use the teacher model to generate pseudo labels on unlabeled images. 3.5B weakly labeled Instagram 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. 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. 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. You signed in with another tab or window. This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. Self-Training With Noisy Student Improves ImageNet Classification. Edit social preview. 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. [68, 24, 55, 22]. Noisy Students performance improves with more unlabeled data. The most interesting image is shown on the right of the first row. The algorithm is basically self-training, a method in semi-supervised learning (. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Summarization_self-training_with_noisy_student_improves_imagenet ImageNet images and use it as a teacher to generate pseudo labels on 300M Infer labels on a much larger unlabeled dataset. - : self-training_with_noisy_student_improves_imagenet_classification Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and If nothing happens, download Xcode and try again. task. Astrophysical Observatory. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. We iterate this process by putting back the student as the teacher. IEEE Trans. Infer labels on a much larger unlabeled dataset. 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. Self-Training Noisy Student " " Self-Training . By clicking accept or continuing to use the site, you agree to the terms outlined in our. Self-Training With Noisy Student Improves ImageNet Classification Noisy Student can still improve the accuracy to 1.6%. We also study the effects of using different amounts of unlabeled data. Self-training with Noisy Student improves ImageNet classification 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. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. combination of labeled and pseudo labeled images. . 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. Computer Science - Computer Vision and Pattern Recognition. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Le. Our main results are shown in Table1. Self-mentoring: : A new deep learning pipeline to train a self This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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. Code for Noisy Student Training. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. 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. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical 10687-10698). Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. and surprising gains on robustness and adversarial benchmarks. 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. 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]. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. 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. Add a Self-training 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. Parthasarathi et al. 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 algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. These CVPR 2020 papers are the Open Access versions, provided by the. 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. 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. [^reference-9] [^reference-10] A critical insight was to . Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. 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.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. 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 with noisy student improves imagenet 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. 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. 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. 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. Self-training with Noisy Student improves ImageNet classification This is probably because it is harder to overfit the large unlabeled dataset. The inputs to the algorithm are both labeled and unlabeled images. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Self-Training with Noisy Student Improves ImageNet Classification ImageNet . Self-training with Noisy Student - 10687-10698 Abstract 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. Self-training with Noisy Student improves ImageNet classification. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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. possible. 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]. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images 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. 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 use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. The main use case of knowledge distillation is model compression by making the student model smaller. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Semi-supervised medical image classification with relation-driven self-ensembling model. 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. CLIP: Connecting text and images - OpenAI We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. 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. 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. 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. 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. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Use, Smithsonian 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. The abundance of data on the internet is vast. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. Use Git or checkout with SVN using the web URL. 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. Iterative training is not used here for simplicity. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled 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 We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Chowdhury et al. We then perform data filtering and balancing on this corpus. Copyright and all rights therein are retained by authors or by other copyright holders. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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. Self-training 1 2Self-training 3 4n What is Noisy Student? 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. We then select images that have confidence of the label higher than 0.3. to use Codespaces. 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). Please sign in Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. . Agreement NNX16AC86A, Is ADS down? 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. Abdominal organ segmentation is very important for clinical applications. Self-training with Noisy Student improves ImageNet classification 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]. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. We sample 1.3M images in confidence intervals. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. 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. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. In contrast, the predictions of the model with Noisy Student remain quite stable. 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. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Learn more. 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. Their main goal is to find a small and fast model for deployment. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. 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 the standard augmentation instead of RandAugment in this experiment. . In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. 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. You signed in with another tab or window. A common workaround is to use entropy minimization or ramp up the consistency loss. Do better imagenet models transfer better? All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. 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. 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. 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 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. The comparison is shown in Table 9. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. A number of studies, e.g. Noisy Student (EfficientNet) - huggingface.co Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper.

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