self training with noisy student improves imagenet classificationnicole alexander bio

Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. A tag already exists with the provided branch name. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. If nothing happens, download Xcode and try again. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. 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 experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. unlabeled images. 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. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. 27.8 to 16.1. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. 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%. If nothing happens, download GitHub Desktop and try again. 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. We first report the validation set accuracy on the ImageNet 2012 ILSVRC challenge prediction task as commonly done in literature[35, 66, 23, 69] (see also [55]). 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 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. For instance, on ImageNet-A, Noisy Student achieves 74.2% top-1 accuracy which is approximately 57% more accurate than the previous state-of-the-art model. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Flip probability is the probability that the model changes top-1 prediction for different perturbations. On robustness test sets, it improves ImageNet-A top . mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. 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. Self-training with Noisy Student improves ImageNet classification We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. https://arxiv.org/abs/1911.04252. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. on ImageNet ReaL Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We iterate this process by putting back the student as the teacher. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . to use Codespaces. 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. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . In other words, the student is forced to mimic a more powerful ensemble model. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. 3.5B weakly labeled Instagram images. We iterate this process by putting back the student as the teacher. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. and surprising gains on robustness and adversarial benchmarks. IEEE Trans. Work fast with our official CLI. 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. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In particular, we first perform normal training with a smaller resolution for 350 epochs. team using this approach not only surpasses the top-1 ImageNet accuracy of SOTA models by 1%, it also shows that the robustness of a model also improves. The algorithm is basically self-training, a method in semi-supervised learning (. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. supervised model from 97.9% accuracy to 98.6% accuracy. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. These significant gains in robustness in ImageNet-C and ImageNet-P are surprising because our models were not deliberately optimizing for robustness (e.g., via data augmentation). It implements SemiSupervised Learning with Noise to create an Image Classification. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. The abundance of data on the internet is vast. CLIP (Contrastive Language-Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.The idea of zero-data learning dates back over a decade [^reference-8] but until recently was mostly studied in computer vision as a way of generalizing to unseen object categories. 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. 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. 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. . Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin Self-training with Noisy Student. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. Test images on ImageNet-P underwent different scales of perturbations. We determine number of training steps and the learning rate schedule by the batch size for labeled images. sign in The accuracy is improved by about 10% in most settings. 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 - Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Noise Self-training with Noisy Student 1. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 Different kinds of noise, however, may have different effects. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single model. Especially unlabeled images are plentiful and can be collected with ease. Self-Training : Noisy Student : Soft pseudo labels lead to better performance for low confidence data. In contrast, the predictions of the model with Noisy Student remain quite stable. CVPR 2020 Open Access Repository 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. In other words, small changes in the input image can cause large changes to the predictions. (using extra training data). 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. Train a classifier on labeled data (teacher). 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. 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]. Train a classifier on labeled data (teacher). Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. GitHub - google-research/noisystudent: Code for Noisy Student Training SelfSelf-training with Noisy Student improves ImageNet classification Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. 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. Summarization_self-training_with_noisy_student_improves_imagenet This material is presented to ensure timely dissemination of scholarly and technical 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. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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Yalniz et al. 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. 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. 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). Self-Training Noisy Student " " Self-Training . 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. 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]. In typical self-training with the teacher-student framework, noise injection to the student is not used by default, or the role of noise is not fully understood or justified. 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. Works based on pseudo label[37, 31, 60, 1] are similar to self-training, but also suffers the same problem with consistency training, since it relies on a model being trained instead of a converged model with high accuracy to generate pseudo labels. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks. 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. 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. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. Self-Training with Noisy Student Improves ImageNet Classification Self-training with Noisy Student improves ImageNet classification. The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. 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.

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