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. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Zoph et al. Self-Training With Noisy Student Improves ImageNet Classification 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 . We improved it by adding noise to the student to learn beyond the teachers knowledge. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. A tag already exists with the provided branch name. Our procedure went as follows. 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. Selected images from robustness benchmarks ImageNet-A, C and P. Test images from ImageNet-C underwent artificial transformations (also known as common corruptions) that cannot be found on the ImageNet training set. We iterate this process by putting back the student as the teacher. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images The inputs to the algorithm are both labeled and unlabeled images. Work fast with our official CLI. However, manually annotating organs from CT scans is time . EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. 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. The performance consistently drops with noise function removed. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. 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 both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Self-training with Noisy Student improves ImageNet classification 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. We find that Noisy Student is better with an additional trick: data balancing. Self-training Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. On, International journal of molecular sciences. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Chowdhury et al. Learn more. Code is available at https://github.com/google-research/noisystudent. 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. 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 use a resolution of 800x800 in this experiment. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. 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 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 . IEEE Transactions on Pattern Analysis and Machine Intelligence. 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. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. Do better imagenet models transfer better? 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. Self-Training for Natural Language Understanding! This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Different types of. sign in Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. Self-training with Noisy Student improves ImageNet classification As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. student is forced to learn harder from the pseudo labels. This model investigates a new method. Models are available at this https URL. Self-training with Noisy Student improves ImageNet classification 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. 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. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Our work is based on self-training (e.g.,[59, 79, 56]). A tag already exists with the provided branch name. https://arxiv.org/abs/1911.04252. In contrast, the predictions of the model with Noisy Student remain quite stable. Papers With Code is a free resource with all data licensed under. 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. In terms of methodology, 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. FixMatch-LS: Semi-supervised skin lesion classification with label Note that these adversarial robustness results are not directly comparable to prior works since we use a large input resolution of 800x800 and adversarial vulnerability can scale with the input dimension[17, 20, 19, 61]. tsai - Noisy student Self-training 1 2Self-training 3 4n What is Noisy Student? Especially unlabeled images are plentiful and can be collected with ease. 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. 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. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 3.5B weakly labeled Instagram images. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Self-Training With Noisy Student Improves ImageNet Classification Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. If nothing happens, download Xcode and try again. 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. 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. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. 10687-10698). [57] used self-training for domain adaptation. 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. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Are labels required for improving adversarial robustness? Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Train a classifier on labeled data (teacher). 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. Our main results are shown in Table1. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. 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). Iterative training is not used here for simplicity. 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. Especially unlabeled images are plentiful and can be collected with ease. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. 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. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. 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 By clicking accept or continuing to use the site, you agree to the terms outlined in our. et al. 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 this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. Different kinds of noise, however, may have different effects. We also list EfficientNet-B7 as a reference. Self-Training With Noisy Student Improves ImageNet Classification. Code for Noisy Student Training. We iterate this process by putting back the student as the teacher. 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. Similar to[71], we fix the shallow layers during finetuning. Noisy Student leads to significant improvements across all model sizes for EfficientNet. During the generation of the pseudo Self-Training : Noisy Student : . Self-training with Noisy Student improves ImageNet classification Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. In particular, we first perform normal training with a smaller resolution for 350 epochs. 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. The proposed use of distillation to only handle easy instances allows for a more aggressive trade-off in the student size, thereby reducing the amortized cost of inference and achieving better accuracy than standard distillation. 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. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. 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. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. We use the standard augmentation instead of RandAugment in this experiment. putting back the student as the teacher. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. You signed in with another tab or window. We iterate this process by putting back the student as the teacher. self-mentoring outperforms data augmentation and self training. We iterate this process by putting back the student as the teacher. Noisy Student can still improve the accuracy to 1.6%. 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. (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. (using extra training data). The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. For more information about the large architectures, please refer to Table7 in Appendix A.1. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Self-training with Noisy Student improves ImageNet classification 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. CVPR 2020 Open Access Repository IEEE Trans. Self-training with Noisy Student improves ImageNet classification ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. . But training robust supervised learning models is requires this step. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards Self-training with Noisy Student improves ImageNet classification. 27.8 to 16.1. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. We iterate this process by Train a larger classifier on the combined set, adding noise (noisy student). 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. Code is available at https://github.com/google-research/noisystudent. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). 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. 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. Not only our method improves standard ImageNet accuracy, it also improves classification robustness on much harder test sets by large margins: ImageNet-A[25] top-1 accuracy from 16.6% to 74.2%, ImageNet-C[24] mean corruption error (mCE) from 45.7 to 31.2 and ImageNet-P[24] mean flip rate (mFR) from 27.8 to 16.1. We present a simple self-training method that achieves 87.4 During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the inception architecture for computer vision, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, EfficientNet: rethinking model scaling for convolutional neural networks, Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results, H. Touvron, A. Vedaldi, M. Douze, and H. Jgou, Fixing the train-test resolution discrepancy, V. Verma, A. Lamb, J. Kannala, Y. Bengio, and D. Lopez-Paz, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), J. Weston, F. Ratle, H. Mobahi, and R. Collobert, Deep learning via semi-supervised embedding, Q. Xie, Z. Dai, E. Hovy, M. Luong, and Q. V. Le, Unsupervised data augmentation for consistency training, S. Xie, R. Girshick, P. Dollr, Z. Tu, and K. He, Aggregated residual transformations for deep neural networks, I. The width. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. It can be seen that masks are useful in improving classification performance. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Yalniz et al. 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). Hence the total number of images that we use for training a student model is 130M (with some duplicated images). CLIP: Connecting text and images - OpenAI Semi-supervised medical image classification with relation-driven self-ensembling model. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Self-training with Noisy Student improves ImageNet classification Use Git or checkout with SVN using the web URL. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 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 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. The results also confirm that vision models can benefit from Noisy Student even without iterative training. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. In the following, we will first describe experiment details to achieve our results. [68, 24, 55, 22]. 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. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). In this section, we study the importance of noise and the effect of several noise methods used in our model. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. This is probably because it is harder to overfit the large unlabeled dataset. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. Why Self-training with Noisy Students beats SOTA Image 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. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). 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 found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. We use the same architecture for the teacher and the student and do not perform iterative training. 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. The main use case of knowledge distillation is model compression by making the student model smaller. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Self-mentoring: : A new deep learning pipeline to train a self Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. Parthasarathi et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Train a larger classifier on the combined set, adding noise (noisy student). 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. Self-Training With Noisy Student Improves ImageNet Classification Abstract: 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.