This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. It implements SemiSupervised Learning with Noise to create an Image Classification. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Self-training with Noisy Student. Especially unlabeled images are plentiful and can be collected with ease. On, International journal of molecular sciences. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. 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. 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 an implementation of Noisy Student Training on SVHN, which boosts the performance of a Please 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. The most interesting image is shown on the right of the first row. Agreement NNX16AC86A, Is ADS down? 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. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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]). 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. 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. 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. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. First, a teacher model is trained in a supervised fashion. 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. 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. Different types of. possible. Iterative training is not used here for simplicity. 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. 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. In other words, small changes in the input image can cause large changes to the predictions. 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. [68, 24, 55, 22]. You signed in with another tab or window. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 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. A tag already exists with the provided branch name. 3429-3440. . 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. Add a We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. Soft pseudo labels lead to better performance for low confidence data. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Astrophysical Observatory. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Code is available at https://github.com/google-research/noisystudent. 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. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. 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. We do not tune these hyperparameters extensively since our method is highly robust to them. Then, that teacher is used to label the unlabeled data. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Image Classification sign in Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. . 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. During the generation of the pseudo 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. Le. 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 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. 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. Our procedure went as follows. 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. However, manually annotating organs from CT scans is time . Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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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. Train a classifier on labeled data (teacher). Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. On . 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. (using extra training data). On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. The architectures for the student and teacher models can be the same or different. 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. 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. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. 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. 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. Self-training first uses labeled data to train a good teacher model, then use the teacher model to label unlabeled data and finally use the labeled data and unlabeled data to jointly train a student 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. Do imagenet classifiers generalize to imagenet? 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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). This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. ; 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. Infer labels on a much larger unlabeled dataset. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We use the same architecture for the teacher and the student and do not perform iterative training. If nothing happens, download Xcode and try again. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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. Due to duplications, there are only 81M unique images among these 130M images. 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. 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. 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 each class, we select at most 130K images that have the highest confidence. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by putting back the student as the teacher. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. It is expensive and must be done with great care. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. 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. (or is it just me), Smithsonian Privacy This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. The comparison is shown in Table 9. 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 performance drops when we further reduce it. For RandAugment, we apply two random operations with the magnitude set to 27. Zoph et al. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Noisy Student can still improve the accuracy to 1.6%. Self-training with Noisy Student improves ImageNet classification Abstract. task. unlabeled images. 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. Finally, in the above, we say that the pseudo labels can be soft or hard. Code for Noisy Student Training. Their main goal is to find a small and fast model for deployment. 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. While removing noise leads to a much lower training loss for labeled images, we observe that, for unlabeled images, removing noise leads to a smaller drop in training loss. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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. all 12, Image Classification If nothing happens, download GitHub Desktop and try again. augmentation, dropout, stochastic depth to the student so that the noised (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. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. to noise the student. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. 10687-10698 Abstract Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Self-training with Noisy Student improves ImageNet classification. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. 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. It can be seen that masks are useful in improving classification performance. IEEE Transactions on Pattern Analysis and Machine Intelligence. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . 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]. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. When data augmentation noise is used, the student must ensure that a translated image, for example, should have the same category with a non-translated image. There was a problem preparing your codespace, please try again. Work fast with our official CLI. The results are shown in Figure 4 with the following observations: (1) Soft pseudo labels and hard pseudo labels can both lead to great improvements with in-domain unlabeled images i.e., high-confidence images. After using the masks generated by teacher-SN, the classification performance improved by 0.2 of AC, 1.2 of SP, and 0.7 of AUC. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Please refer to [24] for details about mCE and AlexNets error rate. We use stochastic depth[29], dropout[63] and RandAugment[14]. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Learn more. If nothing happens, download GitHub Desktop and try again. self-mentoring outperforms data augmentation and self training. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and Flip probability is the probability that the model changes top-1 prediction for different perturbations. Significantly, after using the masks generated by student-SN, the classification performance improved by 0.9 of AC, 0.7 of SE, and 0.9 of AUC. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. unlabeled 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]. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. 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. 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 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. 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.
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self training with noisy student improves imagenet classification