Binghamton Police Department, Dewalt Orbital Sander Won't Turn On, Beat Bobby Flay Losses, Articles S

You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. 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. We also study the effects of using different amounts of unlabeled data. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. self-mentoring outperforms data augmentation and self training. Notice, Smithsonian Terms of Noisy StudentImageNetEfficientNet-L2state-of-the-art. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. 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. On ImageNet-P, it leads to an mean flip rate (mFR) of 17.8 if we use a resolution of 224x224 (direct comparison) and 16.1 if we use a resolution of 299x299.111For EfficientNet-L2, we use the model without finetuning with a larger test time resolution, since a larger resolution results in a discrepancy with the resolution of data and leads to degraded performance on ImageNet-C and ImageNet-P. Here we study how to effectively use out-of-domain data. The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. 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. If nothing happens, download Xcode and try again. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. . all 12, Image Classification over the JFT dataset to predict a label for each image. We use the labeled images to train a teacher model using the standard cross entropy loss. A self-training method that better adapt to the popular two stage training pattern for multi-label text classification under a semi-supervised scenario by continuously finetuning the semantic space toward increasing high-confidence predictions, intending to further promote the performance on target tasks. We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. 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. 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. A common workaround is to use entropy minimization or ramp up the consistency loss. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. 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. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. The main use case of knowledge distillation is model compression by making the student model smaller. We do not tune these hyperparameters extensively since our method is highly robust to them. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 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. We present a simple self-training method that achieves 87.4 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. This invariance constraint reduces the degrees of freedom in the model. 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. First, a teacher model is trained in a supervised fashion. Our main results are shown in Table1. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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. The performance drops when we further reduce it. w Summary of key results compared to previous state-of-the-art models. 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. Copyright and all rights therein are retained by authors or by other copyright holders. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. There was a problem preparing your codespace, please try again. 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. 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. Self-training with noisy student improves imagenet classification. Similar to[71], we fix the shallow layers during finetuning. 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. Noise Self-training with Noisy Student 1. Self-Training achieved the state-of-the-art in ImageNet classification within the framework of Noisy Student [1]. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model We duplicate images in classes where there are not enough images. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. Please refer to [24] for details about mCE and AlexNets error rate. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. supervised model from 97.9% accuracy to 98.6% accuracy. This paper proposes to search for an architectural building block on a small dataset and then transfer the block to a larger dataset and introduces a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. 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. 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. Papers With Code is a free resource with all data licensed under. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. 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. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. On ImageNet-C, it reduces mean corruption error (mCE) from 45.7 to 31.2. 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). Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. On . We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. Agreement NNX16AC86A, Is ADS down? 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. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. labels, the teacher is not noised so that the pseudo labels are as good as Imaging, 39 (11) (2020), pp. Self-training with Noisy Student improves ImageNet classification. Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet On, International journal of molecular sciences. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. 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. 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. . We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. 3429-3440. . Finally, for classes that have less than 130K images, we duplicate some images at random so that each class can have 130K images. ImageNet . . Especially unlabeled images are plentiful and can be collected with ease. 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. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. For more information about the large architectures, please refer to Table7 in Appendix A.1. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. In particular, we first perform normal training with a smaller resolution for 350 epochs. 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. Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. Whether the model benefits from more unlabeled data depends on the capacity of the model since a small model can easily saturate, while a larger model can benefit from more data. 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. 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. Do imagenet classifiers generalize to imagenet? Figure 1(b) shows images from ImageNet-C and the corresponding predictions. on ImageNet, which is 1.0 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. 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. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Self-training with Noisy Student improves ImageNet classification Abstract. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. 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). (using extra training data). Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. 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. 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. Self-training 1 2Self-training 3 4n What is Noisy Student? Le. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. 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. The comparison is shown in Table 9. This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. team using this approach not only surpasses the top-1 ImageNet accuracy of SOTA models by 1%, it also shows that the robustness of a model also improves. Please We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. In other words, the student is forced to mimic a more powerful ensemble model. (or is it just me), Smithsonian Privacy 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 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 contrast, the predictions of the model with Noisy Student remain quite stable. We use EfficientNet-B4 as both the teacher and the student. 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. possible. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. Work fast with our official CLI. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. We improved it by adding noise to the student to learn beyond the teachers knowledge. Summarization_self-training_with_noisy_student_improves_imagenet_classification. This material is presented to ensure timely dissemination of scholarly and technical work. We verify that this is not the case when we use 130M unlabeled images since the model does not overfit the unlabeled set from the training loss. We then use the teacher model to generate pseudo labels on unlabeled images. Med. 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. Code for Noisy Student Training. For simplicity, we experiment with using 1128,164,132,116,14 of the whole data by uniformly sampling images from the the unlabeled set though taking the images with highest confidence leads to better results. 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. on ImageNet ReaL IEEE Transactions on Pattern Analysis and Machine Intelligence. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. Code is available at https://github.com/google-research/noisystudent. [68, 24, 55, 22]. 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. 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. We use the same architecture for the teacher and the student and do not perform iterative training. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. sign in Our work is based on self-training (e.g.,[59, 79, 56]). Code for Noisy Student Training. We iterate this process by putting back the student as the teacher. 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). augmentation, dropout, stochastic depth to the student so that the noised Semi-supervised medical image classification with relation-driven self-ensembling model. 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. Then, that teacher is used to label the unlabeled data. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. A tag already exists with the provided branch name. The architectures for the student and teacher models can be the same or different. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. 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. 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. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. 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.