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. 2023.3.1_2 - The best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Their main goal is to find a small and fast model for deployment. ImageNet-A top-1 accuracy from 16.6 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. Since we use soft pseudo labels generated from the teacher model, when the student is trained to be exactly the same as the teacher model, the cross entropy loss on unlabeled data would be zero and the training signal would vanish. combination of labeled and pseudo labeled images. Especially unlabeled images are plentiful and can be collected with ease. PDF Self-Training with Noisy Student Improves ImageNet Classification We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. self-mentoring outperforms data augmentation and self training. Train a classifier on labeled data (teacher). They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. It is expensive and must be done with great care. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. et al. Parthasarathi et al. 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. Summarization_self-training_with_noisy_student_improves_imagenet We also list EfficientNet-B7 as a reference. 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]). 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. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. Self-training with Noisy Student improves ImageNet classification During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. 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. Self-training with Noisy Student - 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. Especially unlabeled images are plentiful and can be collected with ease. The accuracy is improved by about 10% in most settings. 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. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. Self-training with Noisy Student improves ImageNet classification. 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. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet sign in Different kinds of noise, however, may have different effects. A tag already exists with the provided branch name. CVPR 2020 Open Access Repository 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. . The abundance of data on the internet is vast. 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. 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. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. 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. 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. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. If nothing happens, download Xcode and try again. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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). In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. 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]. student is forced to learn harder from the pseudo labels. 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. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. A tag already exists with the provided branch name. 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. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. Code for Noisy Student Training. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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. Self-training with Noisy Student improves ImageNet classification 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. 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. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. On, International journal of molecular sciences. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 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. These CVPR 2020 papers are the Open Access versions, provided by the. 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 call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. We evaluate the best model, that achieves 87.4% top-1 accuracy, on three robustness test sets: ImageNet-A, ImageNet-C and ImageNet-P. ImageNet-C and P test sets[24] include images with common corruptions and perturbations such as blurring, fogging, rotation and scaling. Although noise may appear to be limited and uninteresting, when it is applied to unlabeled data, it has a compound benefit of enforcing local smoothness in the decision function on both labeled and unlabeled data. Finally, in the above, we say that the pseudo labels can be soft or hard. on ImageNet ReaL 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Our procedure went as follows. Work fast with our official CLI. We also study the effects of using different amounts of unlabeled data. Here we use unlabeled images to improve the state-of-the-art ImageNet accuracy and show that the accuracy gain has an outsized impact on robustness. We then select images that have confidence of the label higher than 0.3. Then, that teacher is used to label the unlabeled data. Self-Training With Noisy Student Improves ImageNet Classification 3.5B weakly labeled Instagram images. 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. Learn more. 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]. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. 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]. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. 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. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. Self-training with Noisy Student improves ImageNet classication Qizhe Xie 1, Minh-Thang Luong , Eduard Hovy2, Quoc V. Le1 1Google Research, Brain Team, 2Carnegie Mellon University fqizhex, thangluong, qvlg@google.com, hovy@cmu.edu Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . The performance drops when we further reduce it. The width. We sample 1.3M images in confidence intervals. 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. But training robust supervised learning models is requires this step. 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. Why Self-training with Noisy Students beats SOTA Image classification 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 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. 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. This model investigates a new method. 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. We use stochastic depth[29], dropout[63] and RandAugment[14]. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. 3429-3440. . Self-training with Noisy Student - Medium 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. Use Git or checkout with SVN using the web URL. 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. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Self-training with Noisy Student improves ImageNet classification If nothing happens, download Xcode and try again. First, a teacher model is trained in a supervised fashion. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Noisy StudentImageNetEfficientNet-L2state-of-the-art. 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. We start with the 130M unlabeled images and gradually reduce the number of images. 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. Noisy Student Training seeks to improve on self-training and distillation in two ways. In the following, we will first describe experiment details to achieve our results. There was a problem preparing your codespace, please try again. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The main use case of knowledge distillation is model compression by making the student model smaller. We use the same architecture for the teacher and the student and do not perform iterative training. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. 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. Self-training with Noisy Student improves ImageNet classification Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. 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. Use, Smithsonian 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]. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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. 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. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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. Self-training with Noisy Student improves ImageNet classification Abstract. 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. 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. Do better imagenet models transfer better? The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Self-Training With Noisy Student Improves ImageNet Classification (using extra training data). We then use the teacher model to generate pseudo labels on unlabeled images. 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. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Self-training was previously used to improve ResNet-50 from 76.4% to 81.2% top-1 accuracy[76] which is still far from the state-of-the-art accuracy. [^reference-9] [^reference-10] A critical insight was to . 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. 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. 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 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). Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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For more information about the large architectures, please refer to Table7 in Appendix A.1. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Self-Training With Noisy Student Improves ImageNet Classification Distillation Survey : Noisy Student | 9to5Tutorial You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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. We iterate this process by putting back the student as the teacher. 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. CLIP: Connecting text and images - OpenAI EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical 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.
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