self training with noisy student improves imagenet classification

task. 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). team using this approach not only surpasses the top-1 ImageNet accuracy of SOTA models by 1%, it also shows that the robustness of a model also improves. 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. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. Self-training with Noisy Student improves ImageNet classification. We apply dropout to the final classification layer with a dropout rate of 0.5. 10687-10698). Self-training with Noisy Student improves ImageNet classification The architectures for the student and teacher models can be the same or different. 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. Noisy Student Explained | Papers With Code We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. CVPR 2020 Open Access Repository Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. 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. Different types of. If nothing happens, download GitHub Desktop and try again. 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. It implements SemiSupervised Learning with Noise to create an Image 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. In this section, we study the importance of noise and the effect of several noise methods used in our model. 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. The accuracy is improved by about 10% in most settings. 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. Then, that teacher is used to label the unlabeled data. Please refer to [24] for details about mFR and AlexNets flip probability. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. 2023.3.1_2 - 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]. Do imagenet classifiers generalize to imagenet? After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. Self-Training with Noisy Student Improves ImageNet Classification Self-training with noisy student improves imagenet classification. tsai - Noisy student The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. Self-training with Noisy Student improves ImageNet classification We do not tune these hyperparameters extensively since our method is highly robust to them. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. For example, without Noisy Student, the model predicts bullfrog for the image shown on the left of the second row, which might be resulted from the black lotus leaf on the water. 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. 27.8 to 16.1. 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. 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. In other words, small changes in the input image can cause large changes to the predictions. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. The most interesting image is shown on the right of the first row. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. Test images on ImageNet-P underwent different scales of perturbations. Code is available at https://github.com/google-research/noisystudent. The inputs to the algorithm are both labeled and unlabeled images. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. We determine number of training steps and the learning rate schedule by the batch size for labeled images. When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single model. We also list EfficientNet-B7 as a reference. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . 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 this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. Self-Training With Noisy Student Improves ImageNet Classification Summarization_self-training_with_noisy_student_improves_imagenet This paper proposes a pipeline, based on a teacher/student paradigm, that leverages a large collection of unlabelled images to improve the performance for a given target architecture, like ResNet-50 or ResNext. Agreement NNX16AC86A, Is ADS down? But during the learning of the student, we inject noise such as data Self-training with Noisy Student improves ImageNet classification Abstract. We then use the teacher model to generate pseudo labels on unlabeled images. Noisy Student Training is a semi-supervised learning approach. Compared to consistency training[45, 5, 74], the self-training / teacher-student framework is better suited for ImageNet because we can train a good teacher on ImageNet using label data. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. Zoph et al. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. 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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. w Summary of key results compared to previous state-of-the-art models. Self-training We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. This work systematically benchmark state-of-the-art methods that use unlabeled data, including domain-invariant, self-training, and self-supervised methods, and shows that their success on WILDS is limited. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). 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 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Yalniz et al. 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. Noisy StudentImageNetEfficientNet-L2state-of-the-art. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. 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. 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]. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. ImageNet-A top-1 accuracy from 16.6 The baseline model achieves an accuracy of 83.2. 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. Med. A. Alemi, Thirty-First AAAI Conference on Artificial Intelligence, C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, C. 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Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and . Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. 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. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks. 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. 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. 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. 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 87.4 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. 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. Distillation Survey : Noisy Student | 9to5Tutorial Self-training with Noisy Student improves ImageNet classification Semi-supervised medical image classification with relation-driven self-ensembling model. 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]. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. [57] used self-training for domain adaptation. 3.5B weakly labeled Instagram images. On robustness test sets, it improves Self-training with Noisy Student improves ImageNet classification Chowdhury et al. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Noisy Student Training seeks to improve on self-training and distillation in two ways. Parthasarathi et al. to noise the student. The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. 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]. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. IEEE Trans. Self-training with Noisy Student improves ImageNet classification 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 Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Noisy Student can still improve the accuracy to 1.6%. 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. 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. Due to the large model size, the training time of EfficientNet-L2 is approximately five times the training time of EfficientNet-B7. et al. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. 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 Similar to[71], we fix the shallow layers during finetuning. These CVPR 2020 papers are the Open Access versions, provided by the. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Models are available at this https URL. It can be seen that masks are useful in improving classification performance. On, International journal of molecular sciences. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We use the same architecture for the teacher and the student and do not perform iterative training. Self-Training With Noisy Student Improves ImageNet Classification . 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. For RandAugment, we apply two random operations with the magnitude set to 27. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. It 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. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. Code for Noisy Student Training. 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. A common workaround is to use entropy minimization or ramp up the consistency loss. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. Here we study how to effectively use out-of-domain data. 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. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. 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%. 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. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. We iterate this process by putting back the student as the teacher. Self-training with Noisy Student improves ImageNet classification 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. If nothing happens, download Xcode and try again. Self-training with Noisy Student improves ImageNet classification (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. However, manually annotating organs from CT scans is time . Add a The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. . We also study the effects of using different amounts of unlabeled data. Why Self-training with Noisy Students beats SOTA Image classification The main use case of knowledge distillation is model compression by making the student model smaller.