self training with noisy student improves imagenet classification

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. The performance consistently drops with noise function removed. 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. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Code is available at https://github.com/google-research/noisystudent. This invariance constraint reduces the degrees of freedom in the model. However, manually annotating organs from CT scans is time . 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. 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. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. For each class, we select at most 130K images that have the highest confidence. 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. 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. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. 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. Agreement NNX16AC86A, Is ADS down? CVPR 2020 Open Access Repository Self-training with Noisy Student - Work fast with our official CLI. 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 used the version from [47], which filtered the validation set of ImageNet. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. Semi-supervised medical image classification with relation-driven self-ensembling model. We also study the effects of using different amounts of unlabeled data. The abundance of data on the internet is vast. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. We find that Noisy Student is better with an additional trick: data balancing. Train a larger classifier on the combined set, adding noise (noisy student). 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. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. 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 use the labeled images to train a teacher model using the standard cross entropy loss. 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. First, we run an EfficientNet-B0 trained on ImageNet[69]. 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. 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]. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. 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. Soft pseudo labels lead to better performance for low confidence data. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin We determine number of training steps and the learning rate schedule by the batch size for labeled images. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. 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. Due to duplications, there are only 81M unique images among these 130M images. . Le. CLIP: Connecting text and images - OpenAI to noise the student. Learn more. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. Classification of Socio-Political Event Data, SLADE: A Self-Training Framework For Distance Metric Learning, Self-Training with Differentiable Teacher, https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. 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. Self-training with Noisy Student improves ImageNet classification 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. In other words, the student is forced to mimic a more powerful ensemble model. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Noisy Student self-training is an effective way to leverage unlabelled datasets and improving accuracy by adding noise to the student model while training so it learns beyond the teacher's knowledge. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. 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. 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%. Noisy Student leads to significant improvements across all model sizes for EfficientNet. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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). combination of labeled and pseudo labeled images. Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. There was a problem preparing your codespace, please try again. 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. The results also confirm that vision models can benefit from Noisy Student even without iterative training. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Hence we use soft pseudo labels for our experiments unless otherwise specified. 3.5B weakly labeled Instagram images. The baseline model achieves an accuracy of 83.2. 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]). unlabeled images , . A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet. 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. 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. PDF Self-Training with Noisy Student Improves ImageNet Classification Different types of. 10687-10698 Abstract 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. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. Abdominal organ segmentation is very important for clinical applications. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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. Then, that teacher is used to label the unlabeled data. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. During this process, we kept increasing the size of the student model to improve the performance. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. Our procedure went as follows. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Please 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 intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct. 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. It can be seen that masks are useful in improving classification performance. On robustness test sets, it improves 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. Self-training 1 2Self-training 3 4n What is Noisy Student? We use stochastic depth[29], dropout[63] and RandAugment[14]. In contrast, the predictions of the model with Noisy Student remain quite stable. Self-Training With Noisy Student Improves ImageNet Classification @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019 . Self-Training with Noisy Student Improves ImageNet Classification Noise Self-training with Noisy Student 1. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. For classes where we have too many images, we take the images with the highest confidence. 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. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Code is available at https://github.com/google-research/noisystudent. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. 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.