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Test images on ImageNet-P underwent different scales of perturbations. 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. 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. A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet. Self-Training With Noisy Student Improves ImageNet Classification. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. 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. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. 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. First, a teacher model is trained in a supervised fashion. 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. EfficientNet with Noisy Student produces correct top-1 predictions (shown in. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards A number of studies, e.g. SelfSelf-training with Noisy Student improves ImageNet classification Are you sure you want to create this branch? Notice, Smithsonian Terms of 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. In contrast, changing architectures or training with weakly labeled data give modest gains in accuracy from 4.7% to 16.6%. As noise injection methods are not used in the student model, and the student model was also small, it is more difficult to make the student better than teacher. 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. When dropout and stochastic depth are used, the teacher model behaves like an ensemble of models (when it generates the pseudo labels, dropout is not used), whereas the student behaves like a single model. task. 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. 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. It is expensive and must be done with great care. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. Our study shows that using unlabeled data improves accuracy and general robustness. Work fast with our official CLI. 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. 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. sign in As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. 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. over the JFT dataset to predict a label for each image. We improved it by adding noise to the student to learn beyond the teachers knowledge. 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. Diagnostics | Free Full-Text | A Collaborative Learning Model for Skin The top-1 and top-5 accuracy are measured on the 200 classes that ImageNet-A includes. This shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. 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. 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. 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. Self-mentoring: : A new deep learning pipeline to train a self Self-Training for Natural Language Understanding! We sample 1.3M images in confidence intervals. 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 then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. 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. We do not tune these hyperparameters extensively since our method is highly robust to them. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. Noisy Student can still improve the accuracy to 1.6%. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Noisy Student leads to significant improvements across all model sizes for EfficientNet. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We use stochastic depth[29], dropout[63] and RandAugment[14]. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). We then use the teacher model to generate pseudo labels on unlabeled images. Self-training with Noisy Student improves ImageNet classification The hyperparameters for these noise functions are the same for EfficientNet-B7, L0, L1 and L2. 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. on ImageNet, which is 1.0 It is found that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. Finally, in the above, we say that the pseudo labels can be soft or hard. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. 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. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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. Similar to[71], we fix the shallow layers during finetuning. supervised model from 97.9% accuracy to 98.6% accuracy. 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. As can be seen from the figure, our model with Noisy Student makes correct predictions for images under severe corruptions and perturbations such as snow, motion blur and fog, while the model without Noisy Student suffers greatly under these conditions. Infer labels on a much larger unlabeled dataset. 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. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. unlabeled images , . 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 then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Iterative training is not used here for simplicity. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. We present a simple self-training method that achieves 87.4 "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. 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. Self-training with Noisy Student - Flip probability is the probability that the model changes top-1 prediction for different perturbations. 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. 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. For a small student model, using our best model Noisy Student (EfficientNet-L2) as the teacher model leads to more improvements than using the same model as the teacher, which shows that it is helpful to push the performance with our method when small models are needed for deployment. We iterate this process by putting back the student as the teacher. 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. The architectures for the student and teacher models can be the same or different. augmentation, dropout, stochastic depth to the student so that the noised (using extra training data). 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. sign in 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. This material is presented to ensure timely dissemination of scholarly and technical work. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. Models are available at this https URL. [68, 24, 55, 22]. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. Imaging, 39 (11) (2020), pp. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Chowdhury et al. Le. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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It can be seen that masks are useful in improving classification performance. (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 investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. Self-training with Noisy Student improves ImageNet classification Then, that teacher is used to label the unlabeled data. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model We use the same architecture for the teacher and the student and do not perform iterative training. CLIP: Connecting text and images - OpenAI Please refer to [24] for details about mFR and AlexNets flip probability. On, International journal of molecular sciences. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. For classes where we have too many images, we take the images with the highest confidence. Image Classification