Are labels required for improving adversarial robustness? We use the standard augmentation instead of RandAugment in this experiment. This is why "Self-training with Noisy Student improves ImageNet classification" written by Qizhe Xie et al makes me very happy. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Self-training with Noisy Student improves ImageNet classification. 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. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Self-training with Noisy Student improves ImageNet classification Original paper: https://arxiv.org/pdf/1911.04252.pdf Authors: Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le HOYA012 Introduction EfficientNet ImageNet SOTA EfficientNet Add a Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). 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. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. The most interesting image is shown on the right of the first row. 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. Similar to[71], we fix the shallow layers during finetuning. Self-Training With Noisy Student Improves ImageNet Classification Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 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. over the JFT dataset to predict a label for each image. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 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. For smaller models, we set the batch size of unlabeled images to be the same as the batch size of labeled images. The performance drops when we further reduce it. The baseline model achieves an accuracy of 83.2. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. If nothing happens, download Xcode and try again. Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. 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. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. We then train a larger EfficientNet as a student model on the unlabeled images. We find that Noisy Student is better with an additional trick: data balancing. (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. 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%. As shown in Figure 1, Noisy Student leads to a consistent improvement of around 0.8% for all model sizes. Qizhe Xie, Eduard Hovy, Minh-Thang Luong, Quoc V. Le. We then perform data filtering and balancing on this corpus. putting back the student as the teacher. 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. In this section, we study the importance of noise and the effect of several noise methods used in our model. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. - : self-training_with_noisy_student_improves_imagenet_classification 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. mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. This model investigates a new method for incorporating unlabeled data into a supervised learning pipeline. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. Although the images in the dataset have labels, we ignore the labels and treat them as unlabeled data. Self-training with Noisy Student. . This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . 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. 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. The accuracy is improved by about 10% in most settings. 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 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. 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. 27.8 to 16.1. 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. By clicking accept or continuing to use the site, you agree to the terms outlined in our. Here we study if it is possible to improve performance on small models by using a larger teacher model, since small models are useful when there are constraints for model size and latency in real-world applications. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. [68, 24, 55, 22]. For unlabeled images, we set the batch size to be three times the batch size of labeled images for large models, including EfficientNet-B7, L0, L1 and L2. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. The comparison is shown in Table 9. Next, with the EfficientNet-L0 as the teacher, we trained a student model EfficientNet-L1, a wider model than L0. We iterate this process by putting back the student as the teacher. Noisy Student can still improve the accuracy to 1.6%. 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. 10687-10698 Abstract This model investigates a new method. We sample 1.3M images in confidence intervals. We used the version from [47], which filtered the validation set of ImageNet. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. Learn more. . We also list EfficientNet-B7 as a reference. Train a classifier on labeled data (teacher). During the generation of the pseudo With Noisy Student, the model correctly predicts dragonfly for the image. ImageNet . Noisy Students performance improves with more unlabeled data. 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. Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M 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 . Due to duplications, there are only 81M unique images among these 130M images. This is probably because it is harder to overfit the large unlabeled dataset. 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. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. We improved it by adding noise to the student to learn beyond the teachers knowledge. 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. 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. Self-Training With Noisy Student Improves ImageNet Classification @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019 . Our main results are shown in Table1. During this process, we kept increasing the size of the student model to improve the performance. 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. augmentation, dropout, stochastic depth to the student so that the noised Self-training with noisy student improves imagenet classification. Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. Use, Smithsonian Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. This invariance constraint reduces the degrees of freedom in the model. 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. 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]). We have also observed that using hard pseudo labels can achieve as good results or slightly better results when a larger teacher is used. all 12, Image Classification To achieve this result, we first train an EfficientNet model on labeled Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. Iterative training is not used here for simplicity. 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. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. 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]. 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. 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. The algorithm is basically self-training, a method in semi-supervised learning (. 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. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Do better imagenet models transfer better? 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. . 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. The abundance of data on the internet is vast. combination of labeled and pseudo labeled images. (using extra training data). Code is available at https://github.com/google-research/noisystudent. Med. possible. Noise Self-training with Noisy Student 1. Code is available at https://github.com/google-research/noisystudent. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Hence, a question that naturally arises is why the student can outperform the teacher with soft pseudo labels. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Work fast with our official CLI. Imaging, 39 (11) (2020), pp. 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. On robustness test sets, it improves ImageNet-A top . We thank the Google Brain team, Zihang Dai, Jeff Dean, Hieu Pham, Colin Raffel, Ilya Sutskever and Mingxing Tan for insightful discussions, Cihang Xie for robustness evaluation, Guokun Lai, Jiquan Ngiam, Jiateng Xie and Adams Wei Yu for feedbacks on the draft, Yanping Huang and Sameer Kumar for improving TPU implementation, Ekin Dogus Cubuk and Barret Zoph for help with RandAugment, Yanan Bao, Zheyun Feng and Daiyi Peng for help with the JFT dataset, Olga Wichrowska and Ola Spyra for help with infrastructure. 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 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. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. It can be seen that masks are useful in improving classification performance. A tag already exists with the provided branch name. 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. Computer Science - Computer Vision and Pattern Recognition. Papers With Code is a free resource with all data licensed under. 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. There was a problem preparing your codespace, please try again. Figure 1(c) shows images from ImageNet-P and the corresponding predictions. 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. 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. Code for Noisy Student Training. A tag already exists with the provided branch name. 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. Aerial Images Change Detection, Multi-Task Self-Training for Learning General Representations, Self-Training Vision Language BERTs with a Unified Conditional Model, 1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality 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. We iterate this process by putting back the student as the teacher. The results also confirm that vision models can benefit from Noisy Student even without iterative training. 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]. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. Self-Training With Noisy Student Improves ImageNet Classification. Using self-training with Noisy Student, together with 300M unlabeled images, we improve EfficientNets[69] ImageNet top-1 accuracy to 87.4%. If nothing happens, download GitHub Desktop and try again. However, in the case with 130M unlabeled images, with noise function removed, the performance is still improved to 84.3% from 84.0% when compared to the supervised baseline. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. We use stochastic depth[29], dropout[63] and RandAugment[14]. A number of studies, e.g. We present a simple self-training method that achieves 87.4 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 robustness test sets, it improves 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. to noise the student. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative In terms of methodology, This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. Copyright and all rights therein are retained by authors or by other copyright holders. Self-training 1 2Self-training 3 4n What is Noisy Student? On robustness test sets, it improves ImageNet-A top-1 accuracy from 61.0% to .