About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . 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. Different types of. . Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Train a larger classifier on the combined set, adding noise (noisy student). E. Arazo, D. Ortego, P. Albert, N. E. OConnor, and K. McGuinness, Pseudo-labeling and confirmation bias in deep semi-supervised learning, B. Athiwaratkun, M. Finzi, P. Izmailov, and A. G. Wilson, There are many consistent explanations of unlabeled data: why you should average, International Conference on Learning Representations, Advances in Neural Information Processing Systems, D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. Raffel, MixMatch: a holistic approach to semi-supervised learning, Combining labeled and unlabeled data with co-training, C. Bucilu, R. Caruana, and A. Niculescu-Mizil, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, Y. Carmon, A. Raghunathan, L. Schmidt, P. Liang, and J. C. Duchi, Unlabeled data improves adversarial robustness, Semi-supervised learning (chapelle, o. et al., eds. , have shown that computer vision models lack robustness. 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. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Code is available at https://github.com/google-research/noisystudent. 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. We start with the 130M unlabeled images and gradually reduce the number of images. There was a problem preparing your codespace, please try again. The abundance of data on the internet is vast. to noise the student. Code for Noisy Student Training. Work fast with our official CLI. 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. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. task. 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. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen, GPipe: efficient training of giant neural networks using pipeline parallelism, A. Iscen, G. Tolias, Y. Avrithis, and O. 10687-10698 Abstract This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. These CVPR 2020 papers are the Open Access versions, provided by the. Next, a larger student model is trained on the combination of all data and achieves better performance than the teacher by itself.OUTLINE:0:00 - Intro \u0026 Overview1:05 - Semi-Supervised \u0026 Transfer Learning5:45 - Self-Training \u0026 Knowledge Distillation10:00 - Noisy Student Algorithm Overview20:20 - Noise Methods22:30 - Dataset Balancing25:20 - Results30:15 - Perturbation Robustness34:35 - Ablation Studies39:30 - Conclusion \u0026 CommentsPaper: https://arxiv.org/abs/1911.04252Code: https://github.com/google-research/noisystudentModels: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnetAbstract:We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. However, manually annotating organs from CT scans is time . Code for Noisy Student Training. This paper reviews the state-of-the-art in both the field of CNNs for image classification and object detection and Autonomous Driving Systems (ADSs) in a synergetic way including a comprehensive trade-off analysis from a human-machine perspective. Image 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. The accuracy is improved by about 10% in most settings. The inputs to the algorithm are both labeled and unlabeled images. 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. Self-Training With Noisy Student Improves ImageNet Classification. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. In the following, we will first describe experiment details to achieve our results. 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. Code is available at https://github.com/google-research/noisystudent. (using extra training data). A number of studies, e.g. . 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 Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet Do imagenet classifiers generalize to imagenet? For classes where we have too many images, we take the images with the highest confidence. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. If nothing happens, download Xcode and try again. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . 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. 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. The main use case of knowledge distillation is model compression by making the student model smaller. 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]. Learn more. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 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 use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le Description: 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. 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 investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. 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. 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 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]. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. We iterate this process by putting back the student as the teacher. Due to duplications, there are only 81M unique images among these 130M images. 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. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We will then show our results on ImageNet and compare them with state-of-the-art models. The abundance of data on the internet is vast. Especially unlabeled images are plentiful and can be collected with ease. IEEE Transactions on Pattern Analysis and Machine Intelligence. 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. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. over the JFT dataset to predict a label for each image. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. But training robust supervised learning models is requires this step. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . If nothing happens, download Xcode and try again. On robustness test sets, it improves Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 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. A tag already exists with the provided branch name. Noisy Student Training is a semi-supervised learning approach. 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]. 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 most interesting image is shown on the right of the first row. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 27.8 to 16.1. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Their purpose is different from ours: to adapt a teacher model on one domain to another. We determine number of training steps and the learning rate schedule by the batch size for labeled 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 smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. Finally, in the above, we say that the pseudo labels can be soft or hard. sign in . Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and 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). Self-training 1 2Self-training 3 4n What is Noisy Student? For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. Train a classifier on labeled data (teacher). 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. We then train a larger EfficientNet as a student model on the We then use the teacher model to generate pseudo labels on unlabeled images. 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 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. Self-training with Noisy Student improves ImageNet classification. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. 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. Our main results are shown in Table1.
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