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This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. We then train a larger EfficientNet as a student model on the supervised model from 97.9% accuracy to 98.6% accuracy. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen, GPipe: efficient training of giant neural networks using pipeline parallelism, A. Iscen, G. Tolias, Y. Avrithis, and O. Noisy StudentImageNetEfficientNet-L2state-of-the-art. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. , have shown that computer vision models lack robustness. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. Noisy Student Explained | Papers With Code Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Different types of. We use the same architecture for the teacher and the student and do not perform iterative training. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. The inputs to the algorithm are both labeled and unlabeled images. 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. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. sign in Are you sure you want to create this branch? We also list EfficientNet-B7 as a reference. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Soft pseudo labels lead to better performance for low confidence data. If nothing happens, download GitHub Desktop and try again. 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. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. 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. 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. These test sets are considered as robustness benchmarks because the test images are either much harder, for ImageNet-A, or the test images are different from the training images, for ImageNet-C and P. For ImageNet-C and ImageNet-P, we evaluate our models on two released versions with resolution 224x224 and 299x299 and resize images to the resolution EfficientNet is trained on. The most interesting image is shown on the right of the first row. 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. 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. By showing the models only labeled images, we limit ourselves from making use of unlabeled images available in much larger quantities to improve accuracy and robustness of state-of-the-art models. 27.8 to 16.1. Our work is based on self-training (e.g.,[59, 79, 56]). 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. 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. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. When data augmentation noise is used, the student must ensure that a translated image, for example, should have the same category with a non-translated image. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. 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. But during the learning of the student, we inject noise such as data We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards [57] used self-training for domain adaptation. Yalniz et al. possible. 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. 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. 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 On robustness test sets, it improves 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. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. Here we study how to effectively use out-of-domain data. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. 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. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. In other words, the student is forced to mimic a more powerful ensemble model. 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. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). 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. We then use the teacher model to generate pseudo labels on unlabeled images. Models are available at this https URL. putting back the student as the teacher. There was a problem preparing your codespace, please try again. 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 . 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. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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. We iterate this process by Edit social preview. et al. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. There was a problem preparing your codespace, please try again. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Flip probability is the probability that the model changes top-1 prediction for different perturbations. Self-training with Noisy Student improves ImageNet classification The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). A tag already exists with the provided branch name. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. For each class, we select at most 130K images that have the highest confidence. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. CLIP: Connecting text and images - OpenAI We then perform data filtering and balancing on this corpus. On, International journal of molecular sciences. 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 our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. We iterate this process by putting back the student as the teacher. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Self-Training With Noisy Student Improves ImageNet Classification Why Self-training with Noisy Students beats SOTA Image classification . mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. To achieve this result, we first train an EfficientNet model on labeled sign in Figure 1(b) shows images from ImageNet-C and the corresponding predictions. [^reference-9] [^reference-10] A critical insight was to . The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. ; 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. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. 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. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. 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. The main use case of knowledge distillation is model compression by making the student model smaller. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. and surprising gains on robustness and adversarial benchmarks. Self-mentoring: : A new deep learning pipeline to train a self 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]. In other words, small changes in the input image can cause large changes to the predictions. Self-training with Noisy Student improves ImageNet classification Train a larger classifier on the combined set, adding noise (noisy student). 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. 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]. 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. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a This is a recurring payment that will happen monthly, If you exceed more than 500 images, they will be charged at a rate of $5 per 500 images. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. 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. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. 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. w Summary of key results compared to previous state-of-the-art models. Notice, Smithsonian Terms of Use Git or checkout with SVN using the web URL. Use Git or checkout with SVN using the web URL. Self-Training With Noisy Student Improves ImageNet Classification. We used the version from [47], which filtered the validation set of ImageNet. Semi-supervised medical image classification with relation-driven self-ensembling model. 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" pytorch implementation. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Self-training with Noisy Student - In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. We do not tune these hyperparameters extensively since our method is highly robust to them. During the generation of the pseudo This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. Please refer to [24] for details about mFR and AlexNets flip probability. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. combination of labeled and pseudo labeled images. Test images on ImageNet-P underwent different scales of perturbations. A semi-supervised segmentation network based on noisy student learning Self-Training with Noisy Student Improves ImageNet Classification For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Ranked #14 on Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. 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. But training robust supervised learning models is requires this step. 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. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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.