In the above experiments, iterative training was used to optimize the accuracy of EfficientNet-L2 but here we skip it as it is difficult to use iterative training for many experiments. 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. The main use case of knowledge distillation is model compression by making the student model smaller. Are you sure you want to create this branch? 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. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Edit social preview. Agreement NNX16AC86A, Is ADS down? Especially unlabeled images are plentiful and can be collected with ease. 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. A tag already exists with the provided branch name. 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. 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. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. 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. (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 have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. - : self-training_with_noisy_student_improves_imagenet_classification As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and Training these networks from only a few annotated examples is challenging while producing manually annotated images that provide supervision is tedious. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. 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. Finally, in the above, we say that the pseudo labels can be soft or hard. on ImageNet, which is 1.0 labels, the teacher is not noised so that the pseudo labels are as good as 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. 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. To achieve strong results on ImageNet, the student model also needs to be large, typically larger than common vision models, so that it can leverage a large number of unlabeled images. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. You signed in with another tab or window. The architectures for the student and teacher models can be the same or different. Models are available at this https URL. task. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. The model with Noisy Student can successfully predict the correct labels of these highly difficult images. 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. 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. 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 found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. We sample 1.3M images in confidence intervals. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. 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 Noisy Student " " Self-Training . 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . 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.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. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. A number of studies, e.g. We then train a student model which minimizes the combined cross entropy loss on both labeled images and unlabeled 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. 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. 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. Yalniz et al. We iterate this process by putting back the student as the teacher. 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. 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Self-training is a form of semi-supervised learning [10] which attempts to leverage unlabeled data to improve classification performance in the limited data regime. We use a resolution of 800x800 in this experiment. Copyright and all rights therein are retained by authors or by other copyright holders. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. 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. We use the standard augmentation instead of RandAugment in this experiment. First, we run an EfficientNet-B0 trained on ImageNet[69]. During this process, we kept increasing the size of the student model to improve the performance. 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. Are labels required for improving adversarial robustness? But training robust supervised learning models is requires this step. 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. Scaling width and resolution by c leads to c2 times training time and scaling depth by c leads to c times training time. The inputs to the algorithm are both labeled and unlabeled images. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Self-training with Noisy Student improves ImageNet classification. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. 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 accuracy is improved by about 10% in most settings. 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. 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. Our work is based on self-training (e.g.,[59, 79, 56]). The most interesting image is shown on the right of the first row. 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. 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. 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. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. Hence we use soft pseudo labels for our experiments unless otherwise specified. On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to . Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, Y. Huang, Y. Cheng, D. Chen, H. Lee, J. Ngiam, Q. V. Le, and Z. Chen, GPipe: efficient training of giant neural networks using pipeline parallelism, A. Iscen, G. Tolias, Y. Avrithis, and O. We iterate this process by putting back the student as the teacher. [2] show that Self-Training is superior to Pre-training with ImageNet Supervised Learning on a few Computer . 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. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. We iterate this process by putting back the student as the teacher. Their framework is highly optimized for videos, e.g., prediction on which frame to use in a video, which is not as general as our work. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. [^reference-9] [^reference-10] A critical insight was to . Image Classification The performance drops when we further reduce it. 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. If nothing happens, download Xcode and try again. 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. 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 is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet and surprising gains on robustness and adversarial benchmarks. 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. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Le. Here we use unlabeled images to improve the state-of-the-art ImageNet accuracy and show that the accuracy gain has an outsized impact on robustness. Code is available at https://github.com/google-research/noisystudent. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Soft pseudo labels lead to better performance for low confidence data. In contrast, the predictions of the model with Noisy Student remain quite stable. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. Code for Noisy Student Training. 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. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. 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 way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. 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. on ImageNet ReaL. We iterate this process by putting back the student as the teacher. all 12, Image Classification The comparison is shown in Table 9. Learn more. We then select images that have confidence of the label higher than 0.3. supervised model from 97.9% accuracy to 98.6% accuracy. Self-training with Noisy Student improves ImageNet classification. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. . Please https://arxiv.org/abs/1911.04252. 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. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. For each class, we select at most 130K images that have the highest confidence. 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. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. We then train a larger EfficientNet as a student model on the Do better imagenet models transfer better? At the top-left image, the model without Noisy Student ignores the sea lions and mistakenly recognizes a buoy as a lighthouse, while the model with Noisy Student can recognize the sea lions. Self-training In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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. ImageNet images and use it as a teacher to generate pseudo labels on 300M In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. 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). 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. The results also confirm that vision models can benefit from Noisy Student even without iterative training. The baseline model achieves an accuracy of 83.2. Noisy student-teacher training for robust keyword spotting, Unsupervised Self-training Algorithm Based on Deep Learning for Optical Noisy StudentImageNetEfficientNet-L2state-of-the-art. We will then show our results on ImageNet and compare them with state-of-the-art models. The width. The performance consistently drops with noise function removed. 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. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. 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. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. In terms of methodology, The best model in our experiments is a result of iterative training of teacher and student by putting back the student as the new teacher to generate new pseudo labels. 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. 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. Astrophysical Observatory. ImageNet-A top-1 accuracy from 16.6 We then use the teacher model to generate pseudo labels on unlabeled images. 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]. 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. Self-training with Noisy Student improves ImageNet classification. We used the version from [47], which filtered the validation set of ImageNet. We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. 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. Self-training 1 2Self-training 3 4n What is Noisy Student? Noisy Student leads to significant improvements across all model sizes for EfficientNet. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. sign in Noisy Student Training seeks to improve on self-training and distillation in two ways. to noise the student. 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. We also list EfficientNet-B7 as a reference. 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. 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. unlabeled images. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from 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. It can be seen that masks are useful in improving classification performance. 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 also study the effects of using different amounts of unlabeled data. Due to duplications, there are only 81M unique images among these 130M images. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. 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. unlabeled images , . (using extra training data). However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. We duplicate images in classes where there are not enough images. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. For classes where we have too many images, we take the images with the highest confidence. Papers With Code is a free resource with all data licensed under. 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. There was a problem preparing your codespace, please try again. (or is it just me), Smithsonian Privacy "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. If nothing happens, download Xcode and try again. Especially unlabeled images are plentiful and can be collected with ease. 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. On . Summarization_self-training_with_noisy_student_improves_imagenet_classification. 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, qvlg@google.com, hovy@cmu.edu Abstract We present Noisy Student Training, a semi-supervised learning approach that works well even when .
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