self training with noisy student improves imagenet classification

Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. As can be seen, our model with Noisy Student makes correct and consistent predictions as images undergone different perturbations while the model without Noisy Student flips predictions frequently. SelfSelf-training with Noisy Student improves ImageNet classification Noisy Student (EfficientNet) - huggingface.co Learn more. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. A number of studies, e.g. For more information about the large architectures, please refer to Table7 in Appendix A.1. unlabeled images , . 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. 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. Summarization_self-training_with_noisy_student_improves_imagenet_classification. See 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 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. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Please We also study the effects of using different amounts of unlabeled data. Noise Self-training with Noisy Student 1. student is forced to learn harder from the pseudo labels. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. Noisy Student can still improve the accuracy to 1.6%. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Why Self-training with Noisy Students beats SOTA Image classification 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. 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. Self-training with Noisy Student improves ImageNet classification Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 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. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. FixMatch-LS: Semi-supervised skin lesion classification with label 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. Models are available at this https URL. Computer Science - Computer Vision and Pattern Recognition. As shown in Table3,4 and5, when compared with the previous state-of-the-art model ResNeXt-101 WSL[44, 48] trained on 3.5B weakly labeled images, Noisy Student yields substantial gains on robustness datasets. 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. 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. It is found that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . Agreement NNX16AC86A, Is ADS down? 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. The most interesting image is shown on the right of the first row. There was a problem preparing your codespace, please try again. possible. In this section, we study the importance of noise and the effect of several noise methods used in our model. 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 this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. Self-training with Noisy Student improves ImageNet classification. 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. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. to use Codespaces. We use the same architecture for the teacher and the student and do not perform iterative training. 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. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. We thank the Google Brain team, Zihang Dai, Jeff Dean, Hieu Pham, Colin Raffel, Ilya Sutskever and Mingxing Tan for insightful discussions, Cihang Xie for robustness evaluation, Guokun Lai, Jiquan Ngiam, Jiateng Xie and Adams Wei Yu for feedbacks on the draft, Yanping Huang and Sameer Kumar for improving TPU implementation, Ekin Dogus Cubuk and Barret Zoph for help with RandAugment, Yanan Bao, Zheyun Feng and Daiyi Peng for help with the JFT dataset, Olga Wichrowska and Ola Spyra for help with infrastructure. Parthasarathi et al. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. Train a larger classifier on the combined set, adding noise (noisy student). 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]. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. 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. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. The width. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. In particular, we first perform normal training with a smaller resolution for 350 epochs. But training robust supervised learning models is requires this step. We start with the 130M unlabeled images and gradually reduce the number of images. sign in The performance drops when we further reduce it. We find that Noisy Student is better with an additional trick: data balancing. 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. 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 improved it by adding noise to the student to learn beyond the teachers knowledge. ; 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. 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. Self-Training With Noisy Student Improves ImageNet Classification The comparison is shown in Table 9. We then select images that have confidence of the label higher than 0.3. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Self-Training With Noisy Student Improves ImageNet Classification. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. Self-Training With Noisy Student Improves ImageNet Classification 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. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. 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 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. During the generation of the pseudo Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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. Self-training with Noisy Student improves ImageNet classification This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Then, that teacher is used to label the unlabeled data. 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. Self-training with Noisy Student improves ImageNet classification. On, International journal of molecular sciences. Self-training with Noisy Student improves ImageNet classification Use Git or checkout with SVN using the web URL. 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. 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. We use EfficientNet-B4 as both the teacher and the student. - : self-training_with_noisy_student_improves_imagenet_classification 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. 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. over the JFT dataset to predict a label for each image. This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. Noisy Student Training seeks to improve on self-training and distillation in two ways. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and Self-training with Noisy Student improves ImageNet classification We present a simple self-training method that achieves 87.4 (using extra training data). The architectures for the student and teacher models can be the same or different. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Code for Noisy Student Training. 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. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images We iterate this process by putting back the student as the teacher. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. There was a problem preparing your codespace, please try again. Similar to[71], we fix the shallow layers during finetuning. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. 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. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Self-training with Noisy Student - This invariance constraint reduces the degrees of freedom in the model. 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 . Train a classifier on labeled data (teacher). Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. On . Noisy Student (B7, L2) means to use EfficientNet-B7 as the student and use our best model with 87.4% accuracy as the teacher model. In other words, the student is forced to mimic a more powerful ensemble model. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. on ImageNet ReaL Self-training with Noisy Student - Medium If nothing happens, download GitHub Desktop and try again. Finally, in the above, we say that the pseudo labels can be soft or hard. Notice, Smithsonian Terms of With Noisy Student, the model correctly predicts dragonfly for the image. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. A tag already exists with the provided branch name. IEEE Transactions on Pattern Analysis and Machine Intelligence. 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 duplicate images in classes where there are not enough images. We use a resolution of 800x800 in this experiment. 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. 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. labels, the teacher is not noised so that the pseudo labels are as good as 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 do not tune these hyperparameters extensively since our method is highly robust to them. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. We used the version from [47], which filtered the validation set of ImageNet. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Self-training [^reference-9] [^reference-10] A critical insight was to . 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. We also list EfficientNet-B7 as a reference. In other words, small changes in the input image can cause large changes to the predictions. 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. Please refer to [24] for details about mFR and AlexNets flip probability.