self training with noisy student improves imagenet classificationvizio sound bar turn off bluetooth

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. 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. ; 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. Code is available at https://github.com/google-research/noisystudent. 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. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. 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. We use EfficientNet-B4 as both the teacher and the student. In this work, we showed that it is possible to use unlabeled images to significantly advance both accuracy and robustness of state-of-the-art ImageNet models. 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. 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. Semi-supervised medical image classification with relation-driven self-ensembling model. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. augmentation, dropout, stochastic depth to the student so that the noised Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 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. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. 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 investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Here we study how to effectively use out-of-domain data. 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. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Self-training 1 2Self-training 3 4n What is Noisy Student? on ImageNet ReaL For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. to use Codespaces. We use EfficientNet-B0 as both the teacher model and the student model and compare using Noisy Student with soft pseudo labels and hard pseudo labels. A novel random matrix theory based damping learner for second order optimisers inspired by linear shrinkage estimation is developed, and it is demonstrated that the derived method works well with adaptive gradient methods such as Adam. We use stochastic depth[29], dropout[63] and RandAugment[14]. Yalniz et al. Especially unlabeled images are plentiful and can be collected with ease. 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%. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. But training robust supervised learning models is requires this step. 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. We then perform data filtering and balancing on this corpus. A common workaround is to use entropy minimization or ramp up the consistency loss. 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. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. 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. Due to duplications, there are only 81M unique images among these 130M images. possible. Abdominal organ segmentation is very important for clinical applications. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. Then, that teacher is used to label the unlabeled data. 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). Their noise model is video specific and not relevant for image classification. Astrophysical Observatory. After testing our models robustness to common corruptions and perturbations, we also study its performance on adversarial perturbations. The mapping from the 200 classes to the original ImageNet classes are available online.222https://github.com/hendrycks/natural-adv-examples/blob/master/eval.py. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model We will then show our results on ImageNet and compare them with state-of-the-art models. sign in For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. Image Classification Hence we use soft pseudo labels for our experiments unless otherwise specified. 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. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. On, International journal of molecular sciences. [68, 24, 55, 22]. Do better imagenet models transfer better? 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, [email protected], [email protected] Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when . task. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. We also list EfficientNet-B7 as a reference. 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]. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. 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. 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. If nothing happens, download GitHub Desktop and try again. The ONCE (One millioN sCenEs) dataset for 3D object detection in the autonomous driving scenario is introduced and a benchmark is provided in which a variety of self-supervised and semi- supervised methods on the ONCE dataset are evaluated. 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. The main difference between Data Distillation and our method is that we use the noise to weaken the student, which is the opposite of their approach of strengthening the teacher by ensembling. The learning rate starts at 0.128 for labeled batch size 2048 and decays by 0.97 every 2.4 epochs if trained for 350 epochs or every 4.8 epochs if trained for 700 epochs. Are labels required for improving adversarial robustness? First, we run an EfficientNet-B0 trained on ImageNet[69]. During the generation of the pseudo The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. to use Codespaces. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. 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. The comparison is shown in Table 9. The performance consistently drops with noise function removed. We do not tune these hyperparameters extensively since our method is highly robust to them. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. Noisy Student can still improve the accuracy to 1.6%. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. Noisy Student leads to significant improvements across all model sizes for EfficientNet. In particular, we first perform normal training with a smaller resolution for 350 epochs. 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. We use the same architecture for the teacher and the student and do not perform iterative training. On robustness test sets, it improves ImageNet-A top . In contrast, the predictions of the model with Noisy Student remain quite stable. 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. The inputs to the algorithm are both labeled and unlabeled images. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new 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. 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. 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. over the JFT dataset to predict a label for each image. The most interesting image is shown on the right of the first row. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. 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. 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. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. EfficientNet-L0 is wider and deeper than EfficientNet-B7 but uses a lower resolution, which gives it more parameters to fit a large number of unlabeled images with similar training speed. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. 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. 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. Noisy Student Training is a semi-supervised learning approach. We used the version from [47], which filtered the validation set of ImageNet. (or is it just me), Smithsonian Privacy corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from A tag already exists with the provided branch name. 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. For classes where we have too many images, we take the images with the highest confidence. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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).

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