self training with noisy student improves imagenet classification

We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Learn more. For RandAugment, we apply two random operations with the magnitude set to 27. Soft pseudo labels lead to better performance for low confidence data. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards CVPR 2020 Open Access Repository 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. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. 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. The most interesting image is shown on the right of the first row. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. 10687-10698 Abstract All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. Self-training with Noisy Student - Medium Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 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. We also list EfficientNet-B7 as a reference. On robustness test sets, it improves Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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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. Work fast with our official CLI. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. In other words, small changes in the input image can cause large changes to the predictions. 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 Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Are labels required for improving adversarial robustness? This model investigates a new method. 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. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. 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. 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). The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. 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. This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. See over the JFT dataset to predict a label for each image. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. 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. 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. Self-Training Noisy Student " " Self-Training . to noise the student. For classes where we have too many images, we take the images with the highest confidence. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. 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. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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. Code for Noisy Student Training. Noisy Student can still improve the accuracy to 1.6%. This is probably because it is harder to overfit the large unlabeled dataset. The main difference between our work and prior works is that we identify the importance of noise, and aggressively inject noise to make the student better. 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. augmentation, dropout, stochastic depth to the student so that the noised Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . 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 iterate this process by putting back the student as the teacher. 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. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. Train a larger classifier on the combined set, adding noise (noisy student). These CVPR 2020 papers are the Open Access versions, provided by the. Finally, in the above, we say that the pseudo labels can be soft or hard. 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 . In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. 3.5B weakly labeled Instagram images. putting back the student as the teacher. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. 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. 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. 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 : Noisy Student : We use the standard augmentation instead of RandAugment in this experiment. on ImageNet ReaL. The algorithm is basically self-training, a method in semi-supervised learning (. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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 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. This invariance constraint reduces the degrees of freedom in the model. [^reference-9] [^reference-10] A critical insight was to . 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. Different kinds of noise, however, may have different effects. Finally, we iterate the algorithm a few times by treating the student as a teacher to generate new pseudo labels and train a new student. Self-training 2023.3.1_2 - For unlabeled images, we set the batch size to be three times the batch size of labeled images for large models, including EfficientNet-B7, L0, L1 and L2. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. It can be seen that masks are useful in improving classification performance. to use Codespaces. 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. We do not tune these hyperparameters extensively since our method is highly robust to them. Their main goal is to find a small and fast model for deployment. Noisy Student Explained | Papers With Code Self-Training With Noisy Student Improves ImageNet Classification arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 Computer Science - Computer Vision and Pattern Recognition. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. possible. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. Models are available at this https URL. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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 also study the effects of using different amounts of unlabeled data. . Figure 1(b) shows images from ImageNet-C and the corresponding predictions. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. Self-Training With Noisy Student Improves ImageNet Classification 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. 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. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. 27.8 to 16.1. Here we study if it is possible to improve performance on small models by using a larger teacher model, since small models are useful when there are constraints for model size and latency in real-world applications. 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. 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. 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]. Please refer to [24] for details about mFR and AlexNets flip probability. all 12, Image Classification Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. If nothing happens, download Xcode and try again. (using extra training data). . In terms of methodology, Are you sure you want to create this branch? 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. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. 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. 10687-10698). Summarization_self-training_with_noisy_student_improves_imagenet_classification. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the 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. 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. . Why Self-training with Noisy Students beats SOTA Image classification Abdominal organ segmentation is very important for clinical applications. Self-training 1 2Self-training 3 4n What is Noisy Student? corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from Self-training with Noisy Student improves ImageNet classification 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. 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. ; 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. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). [57] used self-training for domain adaptation. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. Self-Training With Noisy Student Improves ImageNet Classification self-mentoring outperforms data augmentation and self training. Self-training with Noisy Student improves ImageNet classification Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. Do better imagenet models transfer better? About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . (or is it just me), Smithsonian Privacy 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. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and 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). Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. Especially unlabeled images are plentiful and can be collected with ease. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. Self-Training with Noisy Student Improves ImageNet Classification SelfSelf-training with Noisy Student improves ImageNet classification We iterate this process by putting back the student as the teacher. Self-training with Noisy Student improves ImageNet classification 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. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. 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%. But during the learning of the student, we inject noise such as data 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. First, we run an EfficientNet-B0 trained on ImageNet[69]. [68, 24, 55, 22].

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