self training with noisy student improves imagenet 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. 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. et al. The architectures for the student and teacher models can be the same or different. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. It implements SemiSupervised Learning with Noise to create an Image Classification. 3429-3440. . 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. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. We duplicate images in classes where there are not enough images. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. The most interesting image is shown on the right of the first row. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. Train a classifier on labeled data (teacher). 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. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. 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 call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 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. Code for Noisy Student Training. Self-training with Noisy Student improves ImageNet classification. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. . on ImageNet ReaL 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. In the following, we will first describe experiment details to achieve our results. Edit social preview. 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. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10687-10698, (2020 . This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Image Classification Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Parthasarathi et al. We iterate this process by 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. 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. Soft pseudo labels lead to better performance for low confidence data. Computer Science - Computer Vision and Pattern Recognition. 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. On robustness test sets, it improves Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Test images on ImageNet-P underwent different scales of perturbations. 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 abundance of data on the internet is vast. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. The algorithm is basically self-training, a method in semi-supervised learning (. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical 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. Train a classifier on labeled data (teacher). We find that using a batch size of 512, 1024, and 2048 leads to the same performance. 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. 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. But training robust supervised learning models is requires this step. We then train a larger EfficientNet as a student model on the corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from Code for Noisy Student Training. This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. 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 . Then, that teacher is used to label the unlabeled data. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. IEEE Transactions on Pattern Analysis and Machine Intelligence. The results also confirm that vision models can benefit from Noisy Student even without iterative training. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. 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. Chowdhury et al. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. We iterate this process by putting back the student as the teacher. We used the version from [47], which filtered the validation set of ImageNet. 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). Work fast with our official CLI. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. 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. 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. We apply dropout to the final classification layer with a dropout rate of 0.5. The baseline model achieves an accuracy of 83.2. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. Agreement NNX16AC86A, Is ADS down? 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. student is forced to learn harder from the pseudo labels. As noise injection methods are not used in the student model, and the student model was also small, it is more difficult to make the student better than teacher. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. 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. 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. Please refer to [24] for details about mFR and AlexNets flip probability. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. Train a larger classifier on the combined set, adding noise (noisy student). We will then show our results on ImageNet and compare them with state-of-the-art models. 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 noise the 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. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. Different kinds of noise, however, may have different effects. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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 obtain unlabeled images from the JFT dataset [26, 11], which has around 300M 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. We then use the teacher model to generate pseudo labels on unlabeled images. over the JFT dataset to predict a label for each image. 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. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. IEEE Trans. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. [68, 24, 55, 22]. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. 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. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. task. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. 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. 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. Unlike previous studies in semi-supervised learning that use in-domain unlabeled data (e.g, ., CIFAR-10 images as unlabeled data for a small CIFAR-10 training set), to improve ImageNet, we must use out-of-domain unlabeled data. With Noisy Student, the model correctly predicts dragonfly for the image. putting back the student as the teacher. A number of studies, e.g. 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. Noisy Students performance improves with more unlabeled data. 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]). Astrophysical Observatory. 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. supervised model from 97.9% accuracy to 98.6% accuracy. In particular, we first perform normal training with a smaller resolution for 350 epochs. Infer labels on a much larger unlabeled dataset. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Self-Training Noisy Student " " Self-Training . This invariance constraint reduces the degrees of freedom in the model. Ranked #14 on The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. On . Self-training 1 2Self-training 3 4n What is Noisy Student?

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