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. Are labels required for improving adversarial robustness? Noisy Students performance improves with more unlabeled data. Self-Training With Noisy Student Improves ImageNet Classification PDF Self-Training with Noisy Student Improves ImageNet Classification This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Since we use soft pseudo labels generated from the teacher model, when the student is trained to be exactly the same as the teacher model, the cross entropy loss on unlabeled data would be zero and the training signal would vanish. (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. 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. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. The abundance of data on the internet is vast. 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. Code is available at https://github.com/google-research/noisystudent. We use a resolution of 800x800 in this experiment. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. mFR (mean flip rate) is the weighted average of flip probability on different perturbations, with AlexNets flip probability as a baseline. In particular, we first perform normal training with a smaller resolution for 350 epochs. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. 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. 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. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. ImageNet-A top-1 accuracy from 16.6 As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 3429-3440. . 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. Self-Training With Noisy Student Improves ImageNet Classification Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). 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. - : self-training_with_noisy_student_improves_imagenet_classification Agreement NNX16AC86A, Is ADS down? Chowdhury et al. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. C. Szegedy, S. Ioffe, V. Vanhoucke, and A. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. 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. over the JFT dataset to predict a label for each image. Self-training with Noisy Student improves ImageNet classification Use, Smithsonian You signed in with another tab or window. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 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. (or is it just me), Smithsonian Privacy We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. 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. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. 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. We use EfficientNet-B4 as both the teacher and the student. 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. The results also confirm that vision models can benefit from Noisy Student even without iterative training. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. We iterate this process by Self-training first uses labeled data to train a good teacher model, then use the teacher model to label unlabeled data and finally use the labeled data and unlabeled data to jointly train a student model. 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). The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training 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. Train a classifier on labeled data (teacher). In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. We used the version from [47], which filtered the validation set of ImageNet. But training robust supervised learning models is requires this step. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. We use the standard augmentation instead of RandAugment in this experiment. 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. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Self-training with Noisy Student - Self-Training With Noisy Student Improves ImageNet Classification A semi-supervised segmentation network based on noisy student learning You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Parthasarathi et al. 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. It is expensive and must be done with great care. There was a problem preparing your codespace, please try again. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. ImageNet . For instance, on ImageNet-1k, Layer Grafted Pre-training yields 65.5% Top-1 accuracy in terms of 1% few-shot learning with ViT-B/16, which improves MIM and CL baselines by 14.4% and 2.1% with no bells and whistles. 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. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. Do imagenet classifiers generalize to imagenet? This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. We iterate this process by putting back the student as the teacher. Please refer to [24] for details about mCE and AlexNets error rate. 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. task. Self-training with Noisy Student improves ImageNet classification to use Codespaces. We then select images that have confidence of the label higher than 0.3. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. 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. The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. We do not tune these hyperparameters extensively since our method is highly robust to them. [^reference-9] [^reference-10] A critical insight was to . Imaging, 39 (11) (2020), pp. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. The algorithm is basically self-training, a method in semi-supervised learning (. We first report the validation set accuracy on the ImageNet 2012 ILSVRC challenge prediction task as commonly done in literature[35, 66, 23, 69] (see also [55]). In contrast, the predictions of the model with Noisy Student remain quite stable. 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. 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. Self-training with Noisy Student - Medium Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (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. We present a simple self-training method that achieves 87.4 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. 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. 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. Finally, in the above, we say that the pseudo labels can be soft or hard. 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. Zoph et al. A tag already exists with the provided branch name. 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]. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. sign in Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model 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 contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Iterative training is not used here for simplicity. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . But during the learning of the student, we inject noise such as data Their noise model is video specific and not relevant for image classification. 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]. The baseline model achieves an accuracy of 83.2. 10687-10698). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). We determine number of training steps and the learning rate schedule by the batch size for labeled images. 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. For classes where we have too many images, we take the images with the highest confidence. 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. unlabeled images. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. CLIP: Connecting text and images - OpenAI Self-Training With Noisy Student Improves ImageNet Classification. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. Self-training Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Self-training 1 2Self-training 3 4n What is Noisy Student? Noisy StudentImageNetEfficientNet-L2state-of-the-art. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. and surprising gains on robustness and adversarial benchmarks. We will then show our results on ImageNet and compare them with state-of-the-art models. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. 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. Self-Training for Natural Language Understanding! This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin Chum, Label propagation for deep semi-supervised learning, D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, Semi-supervised learning with deep generative models, Semi-supervised classification with graph convolutional networks. 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. Hence we use soft pseudo labels for our experiments unless otherwise specified. Edit social preview. 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. putting back the student as the teacher. Noise Self-training with Noisy Student 1. https://arxiv.org/abs/1911.04252. 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. all 12, Image Classification A common workaround is to use entropy minimization or ramp up the consistency loss. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. In the following, we will first describe experiment details to achieve our results. Self-training with Noisy Student improves ImageNet classification For each class, we select at most 130K images that have the highest confidence. 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. [68, 24, 55, 22]. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. The paradigm of pre-training on large supervised datasets and fine-tuning the weights on the target task is revisited, and a simple recipe that is called Big Transfer (BiT) is created, which achieves strong performance on over 20 datasets. . Self-mentoring: : A new deep learning pipeline to train a self Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. arXiv:1911.04252v4 [cs.LG] 19 Jun 2020 The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . ImageNet images and use it as a teacher to generate pseudo labels on 300M Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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. Why Self-training with Noisy Students beats SOTA Image classification Do better imagenet models transfer better? Self-training with Noisy Student improves ImageNet classificationCVPR2020, Codehttps://github.com/google-research/noisystudent, Self-training, 1, 2Self-training, Self-trainingGoogleNoisy Student, Noisy Studentstudent modeldropout, stochastic depth andaugmentationteacher modelNoisy Noisy Student, Noisy Student, 1, JFT3ImageNetEfficientNet-B00.3130K130K, EfficientNetbaseline modelsEfficientNetresnet, EfficientNet-B7EfficientNet-L0L1L2, batchsize = 2048 51210242048EfficientNet-B4EfficientNet-L0l1L2350epoch700epoch, 2EfficientNet-B7EfficientNet-L0, 3EfficientNet-L0EfficientNet-L1L0, 4EfficientNet-L1EfficientNet-L2, student modelNoisy, noisystudent modelteacher modelNoisy, Noisy, Self-trainingaugmentationdropoutstochastic depth, Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores., 12/self-training-with-noisy-student-f33640edbab2, EfficientNet-L0EfficientNet-B7B7, EfficientNet-L1EfficientNet-L0, EfficientNetsEfficientNet-L1EfficientNet-L2EfficientNet-L2EfficientNet-B75. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. The performance consistently drops with noise function removed. This is probably because it is harder to overfit the large unlabeled dataset. 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. [57] used self-training for domain adaptation. 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. 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. On . 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. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. 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 . . If nothing happens, download GitHub Desktop and try again. Self-training with Noisy Student improves ImageNet classification Noisy Student Training seeks to improve on self-training and distillation in two ways. Self-Training Noisy Student " " Self-Training . 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. unlabeled images , . . 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. 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. 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. Self-training with Noisy Student improves ImageNet classification We iterate this process by putting back the student as the teacher. Noisy Student can still improve the accuracy to 1.6%. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Self-training with Noisy Student improves ImageNet classification Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. We iterate this process by putting back the student as the teacher. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. 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.

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