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 Abstract. Train a larger classifier on the combined set, adding noise (noisy student). Self-training with Noisy Student improves ImageNet classification. We present a simple self-training method that achieves 87.4 Self-Training with Noisy Student Improves ImageNet Classification We iterate this process by putting back the student as the teacher. 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. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative tsai - Noisy student We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. The algorithm is basically self-training, a method in semi-supervised learning (. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Self-training was previously used to improve ResNet-50 from 76.4% to 81.2% top-1 accuracy[76] which is still far from the state-of-the-art accuracy. In contrast, the predictions of the model with Noisy Student remain quite stable. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment. We then perform data filtering and balancing on this corpus. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Work fast with our official CLI. 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. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). Self-training with Noisy Student improves ImageNet classification Their noise model is video specific and not relevant for image classification. We will then show our results on ImageNet and compare them with state-of-the-art models. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Hence we use soft pseudo labels for our experiments unless otherwise specified. With Noisy Student, the model correctly predicts dragonfly for the image. Ranked #14 on On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Noisy Student Explained | Papers With Code In other words, the student is forced to mimic a more powerful ensemble model. over the JFT dataset to predict a label for each image. Train a classifier on labeled data (teacher). Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Especially unlabeled images are plentiful and can be collected with ease. Self-training with Noisy Student improves ImageNet classification Iterative training is not used here for simplicity. 27.8 to 16.1. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. 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. 3429-3440. . There was a problem preparing your codespace, please try again. 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. Their purpose is different from ours: to adapt a teacher model on one domain to another. sign in It is expensive and must be done with great care. On robustness test sets, it improves Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. The architectures for the student and teacher models can be the same or different. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. 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. Summarization_self-training_with_noisy_student_improves_imagenet_classification. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. Are you sure you want to create this branch? PDF Self-Training with Noisy Student Improves ImageNet Classification 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. Code is available at https://github.com/google-research/noisystudent. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. Soft pseudo labels lead to better performance for low confidence data. Please 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]. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Especially unlabeled images are plentiful and can be collected with ease. Our main results are shown in Table1. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). The main difference between our work and prior works is that we identify the importance of noise, and aggressively inject noise to make the student better. 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. For each class, we select at most 130K images that have the highest confidence. Work fast with our official CLI. If nothing happens, download Xcode and try again. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. In our implementation, labeled images and unlabeled images are concatenated together and we compute the average cross entropy loss. Here we study how to effectively use out-of-domain data. Chowdhury et al. The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. 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. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. 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 apply dropout to the final classification layer with a dropout rate of 0.5. 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. Image Classification corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 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. on ImageNet ReaL Significantly, after using the masks generated by student-SN, the classification performance improved by 0.9 of AC, 0.7 of SE, and 0.9 of AUC. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We iterate this process by putting back the student as the teacher. For RandAugment, we apply two random operations with the magnitude set to 27. FixMatch-LS: Semi-supervised skin lesion classification with label 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. 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. We used the version from [47], which filtered the validation set of ImageNet. 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. Self-training with Noisy Student improves ImageNet classification Self-training with Noisy Student improves ImageNet classification Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. 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 The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. 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. 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 can still improve the accuracy to 1.6%. Models are available at this https URL. 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 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. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 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. See If nothing happens, download GitHub Desktop and try again. In the following, we will first describe experiment details to achieve our results. 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. We find that Noisy Student is better with an additional trick: data balancing. Self-training with Noisy Student improves ImageNet classification We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. 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. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . 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. (Submitted on 11 Nov 2019) We present a simple self-training method that achieves 87.4% top-1 accuracy on ImageNet, which is 1.0% better than the state-of-the-art model that requires 3.5B weakly labeled Instagram images. You signed in with another tab or window. If nothing happens, download GitHub Desktop and try again. Zoph et al. A common workaround is to use entropy minimization or ramp up the consistency loss. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious.