self training with noisy student improves imagenet classification

Self-training with Noisy Student improves ImageNet classification Abstract. to use Codespaces. 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. IEEE Transactions on Pattern Analysis and Machine Intelligence. 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. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. For classes where we have too many images, we take the images with the highest confidence. 10687-10698 Abstract The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative In other words, the student is forced to mimic a more powerful ensemble model. We then select images that have confidence of the label higher than 0.3. ImageNet . 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. 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). 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. 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 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. - : self-training_with_noisy_student_improves_imagenet_classification It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images To achieve this result, we first train an EfficientNet model on labeled Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. Edit social preview. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. Our work is based on self-training (e.g.,[59, 79, 56]). The model with Noisy Student can successfully predict the correct labels of these highly difficult images. 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. The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. on ImageNet ReaL. 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. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. If nothing happens, download GitHub Desktop and try again. We use a resolution of 800x800 in this experiment. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. 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. 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. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. Work fast with our official CLI. 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. Different kinds of noise, however, may have different effects. Add a Are you sure you want to create this branch? and surprising gains on robustness and adversarial benchmarks. Self-Training with Noisy Student Improves ImageNet Classification You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. 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. In particular, we first perform normal training with a smaller resolution for 350 epochs. To date (2020) we will introduce "Noisy Student Training", which is a state-of-the-art model.The idea is to extend self-training and Distillation, a paper that shows that by adding three noises and distilling multiple times, the student model will have better generalization performance than the teacher model. Their noise model is video specific and not relevant for image classification. Summarization_self-training_with_noisy_student_improves_imagenet_classification. Self-training with Noisy Student improves ImageNet classification. A tag already exists with the provided branch name. 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. Here we study how to effectively use out-of-domain data. 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]. , have shown that computer vision models lack robustness. 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. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. sign in We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. In terms of methodology, on ImageNet ReaL Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. Abdominal organ segmentation is very important for clinical applications. We use the labeled images to train a teacher model using the standard cross entropy loss. 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]). In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Noisy Student leads to significant improvements across all model sizes for EfficientNet. On robustness test sets, it improves 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 seeks to improve on self-training and distillation in two ways. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. 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. Infer labels on a much larger unlabeled dataset. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. This is probably because it is harder to overfit the large unlabeled dataset. 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. 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. Learn more. 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 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 Learn more. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. 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 method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Models are available at this https URL. Our main results are shown in Table1. 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). (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. Train a larger classifier on the combined set, adding noise (noisy student). to use Codespaces. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. 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 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. At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. The most interesting image is shown on the right of the first row. 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. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. 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. 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. Noisy Student Training is a semi-supervised learning approach. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. First, a teacher model is trained in a supervised fashion. 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%. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . The accuracy is improved by about 10% in most settings. 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. Copyright and all rights therein are retained by authors or by other copyright holders. We conduct experiments on ImageNet 2012 ILSVRC challenge prediction task since it has been considered one of the most heavily benchmarked datasets in computer vision and that improvements on ImageNet transfer to other datasets. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . Self-Training Noisy Student " " Self-Training . Please Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. 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]. 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. Use, Smithsonian Test images on ImageNet-P underwent different scales of perturbations. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. 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. https://arxiv.org/abs/1911.04252. The comparison is shown in Table 9. Noisy Student can still improve the accuracy to 1.6%. Finally, in the above, we say that the pseudo labels can be soft or hard. to noise the student. During this process, we kept increasing the size of the student model to improve the performance. 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, in: Proceedings of the IEEE/CVF Conference on Computer . Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. 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. The architectures for the student and teacher models can be the same or different. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). 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. 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. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. We iterate this process by putting back the student as the teacher. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. unlabeled images , . We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Then, that teacher is used to label the unlabeled data. 10687-10698). We iterate this process by putting back the student as the teacher. During the generation of the pseudo 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. 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. 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. We do not tune these hyperparameters extensively since our method is highly robust to them. student is forced to learn harder from the 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. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. The performance drops when we further reduce it. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10687-10698, (2020 .

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