self training with noisy student improves imagenet classification self training with noisy student improves imagenet classification

Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. During this process, we kept increasing the size of the student model to improve the performance. Self-Training With Noisy Student Improves ImageNet Classification Especially unlabeled images are plentiful and can be collected with ease. In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. This invariance constraint reduces the degrees of freedom in the model. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. to use Codespaces. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. 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. With Noisy Student, the model correctly predicts dragonfly for the image. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. The results also confirm that vision models can benefit from Noisy Student even without iterative training. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Noise Self-training with Noisy Student 1. et al. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. 2023.3.1_2 - We use the same architecture for the teacher and the student and do not perform iterative training. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. Self-Training for Natural Language Understanding! The model with Noisy Student can successfully predict the correct labels of these highly difficult images. 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. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Test images on ImageNet-P underwent different scales of perturbations. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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. 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]. For each class, we select at most 130K images that have the highest confidence. Self-training with Noisy Student improves ImageNet classification Self-training with Noisy Student improves ImageNet classification Noisy Student leads to significant improvements across all model sizes for EfficientNet. This material is presented to ensure timely dissemination of scholarly and technical work. Infer labels on a much larger unlabeled dataset. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. 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. We improved it by adding noise to the student to learn beyond the teachers knowledge. Here we show the evidence in Table 6, noise such as stochastic depth, dropout and data augmentation plays an important role in enabling the student model to perform better than the teacher. . C. Szegedy, S. Ioffe, V. Vanhoucke, and A. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. 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. On robustness test sets, it improves IEEE Transactions on Pattern Analysis and Machine Intelligence. (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. But during the learning of the student, we inject noise such as data Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. As can be seen from Table 8, the performance stays similar when we reduce the data to 116 of the total data, which amounts to 8.1M images after duplicating. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. In other words, small changes in the input image can cause large changes to the predictions. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Learn more. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. task. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. 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. Specifically, we train the student model for 350 epochs for models larger than EfficientNet-B4, including EfficientNet-L0, L1 and L2 and train the student model for 700 epochs for smaller models. . Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. A common workaround is to use entropy minimization or ramp up the consistency loss. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. labels, the teacher is not noised so that the pseudo labels are as good as Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . The main difference between our method and knowledge distillation is that knowledge distillation does not consider unlabeled data and does not aim to improve the student model. Self-Training Noisy Student " " Self-Training . and surprising gains on robustness and adversarial benchmarks. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Self-training 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. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. 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. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. sign in Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. Here we study how to effectively use out-of-domain data. Self-training with Noisy Student improves ImageNet classification. 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. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . Iterative training is not used here for simplicity. CVPR 2020 Open Access Repository We also study the effects of using different amounts of 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. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. 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. Code is available at https://github.com/google-research/noisystudent. We use EfficientNet-B4 as both the teacher and the student. 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 We do not tune these hyperparameters extensively since our method is highly robust to them. In contrast, the predictions of the model with Noisy Student remain quite stable. The algorithm is basically self-training, a method in semi-supervised learning (. We then use the teacher model to generate pseudo labels on unlabeled images. 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. Learn more. Since a teacher models confidence on an image can be a good indicator of whether it is an out-of-domain image, we consider the high-confidence images as in-domain images and the low-confidence images as out-of-domain images. For instance, on the right column, as the image of the car undergone a small rotation, the standard model changes its prediction from racing car to car wheel to fire engine. Do imagenet classifiers generalize to imagenet? 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. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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. 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 pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Self-training with Noisy Student improves ImageNet classification Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. Notice, Smithsonian Terms of But training robust supervised learning models is requires this step. Are you sure you want to create this branch? arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. Work fast with our official CLI. Self-training 1 2Self-training 3 4n What is Noisy Student? There was a problem preparing your codespace, please try again. Self-training with Noisy Student - Self-training with Noisy Student improves ImageNet classification 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. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 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. Self-training with Noisy Student - Medium Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. Self-Training With Noisy Student Improves ImageNet Classification Self-training with Noisy Student improves ImageNet classification 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. 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. We then select images that have confidence of the label higher than 0.3. To achieve this result, we first train an EfficientNet model on labeled 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. Self-training with Noisy Student improves ImageNet classification. Noisy Students performance improves with more unlabeled data. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). 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 model investigates a new method. Self-Training With Noisy Student Improves ImageNet Classification The main use case of knowledge distillation is model compression by making the student model smaller. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. Self-Training with Noisy Student Improves ImageNet Classification When the student model is deliberately noised it is actually trained to be consistent to the more powerful teacher model that is not noised when it generates pseudo labels. Summarization_self-training_with_noisy_student_improves_imagenet We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. 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. ImageNet images and use it as a teacher to generate pseudo labels on 300M Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. 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.