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]). 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. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. We use EfficientNet-B4 as both the teacher and the student. Noisy StudentImageNetEfficientNet-L2state-of-the-art. CLIP: Connecting text and images - OpenAI These CVPR 2020 papers are the Open Access versions, provided by the. 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. Hence we use soft pseudo labels for our experiments unless otherwise specified. Self-training with Noisy Student improves ImageNet classification. Self-Training With Noisy Student Improves ImageNet Classification. Notice, Smithsonian Terms of 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. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . Distillation Survey : Noisy Student | 9to5Tutorial 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 use a resolution of 800x800 in this experiment. 10687-10698 Abstract 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. Learn more. In other words, the student is forced to mimic a more powerful ensemble model. The comparison is shown in Table 9. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Probably due to the same reason, at =16, EfficientNet-L2 achieves an accuracy of 1.1% under a stronger attack PGD with 10 iterations[43], which is far from the SOTA results. 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. 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. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. The top-1 accuracy is simply the average top-1 accuracy for all corruptions and all severity degrees. [57] used self-training for domain adaptation. The performance consistently drops with noise function removed. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards supervised model from 97.9% accuracy to 98.6% accuracy. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . Copyright and all rights therein are retained by authors or by other copyright holders. 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. Here we study how to effectively use out-of-domain data. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled images. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. A tag already exists with the provided branch name. 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]. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. Self-training 1 2Self-training 3 4n What is Noisy Student? 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. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. et al. On . 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. But during the learning of the student, we inject noise such as data Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Please refer to [24] for details about mFR and AlexNets flip probability. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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. 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. We then use the teacher model to generate pseudo labels on 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. 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. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . 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. This material is presented to ensure timely dissemination of scholarly and technical work. tsai - Noisy student On robustness test sets, it improves ImageNet-A top . 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. 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). Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. Self-Training With Noisy Student Improves ImageNet Classification Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. Self-Training : Noisy Student : 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. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. to noise the student. 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 improves ImageNet classification 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 most interesting image is shown on the right of the first row. 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. Le. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: For ImageNet checkpoints trained by Noisy Student Training, please refer to the EfficientNet github. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Noisy Student Training is a semi-supervised learning approach. 27.8 to 16.1. 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 iterate this process by putting back the student as the teacher. 3429-3440. . We iterate this process by putting back the student as the teacher. We do not tune these hyperparameters extensively since our method is highly robust to them. Their purpose is different from ours: to adapt a teacher model on one domain to another. 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. 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. 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. 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. 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. We then select images that have confidence of the label higher than 0.3. 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). 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. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. It is experimentally validated that, for a target test resolution, using a lower train resolution offers better classification at test time, and a simple yet effective and efficient strategy to optimize the classifier performance when the train and test resolutions differ is proposed. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. This is probably because it is harder to overfit the large unlabeled dataset. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images Not only our method improves standard ImageNet accuracy, it also improves classification robustness on much harder test sets by large margins: ImageNet-A[25] top-1 accuracy from 16.6% to 74.2%, ImageNet-C[24] mean corruption error (mCE) from 45.7 to 31.2 and ImageNet-P[24] mean flip rate (mFR) from 27.8 to 16.1. ImageNet images and use it as a teacher to generate pseudo labels on 300M Train a classifier on labeled data (teacher). 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. FixMatch-LS: Semi-supervised skin lesion classification with label The accuracy is improved by about 10% in most settings. We apply dropout to the final classification layer with a dropout rate of 0.5. sign in 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. 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. 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. 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. Self-training with Noisy Student improves ImageNet classification We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We iterate this process by [^reference-9] [^reference-10] A critical insight was to . If nothing happens, download GitHub Desktop and try again. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. on ImageNet ReaL. 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. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. The baseline model achieves an accuracy of 83.2. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. Self-training with Noisy Student improves ImageNet classification 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. Add a 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. 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. Soft pseudo labels lead to better performance for low confidence data. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Similar to[71], we fix the shallow layers during finetuning. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. It can be seen that masks are useful in improving classification performance. 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.