As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. We iterate this process by putting back the student as 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. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. 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.
SelfSelf-training with Noisy Student improves ImageNet classification The algorithm is basically self-training, a method in semi-supervised learning (. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. We improved it by adding noise to the student to learn beyond the teachers knowledge. Then, EfficientNet-L1 is scaled up from EfficientNet-L0 by increasing width. We first improved the accuracy of EfficientNet-B7 using EfficientNet-B7 as both the teacher and the student. (or is it just me), Smithsonian Privacy A common workaround is to use entropy minimization or ramp up the consistency loss. . Learn more. all 12, Image Classification In our experiments, we observe that soft pseudo labels are usually more stable and lead to faster convergence, especially when the teacher model has low accuracy. 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. 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. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. to use Codespaces. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). We use stochastic depth[29], dropout[63] and RandAugment[14]. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. During the generation of the pseudo The results also confirm that vision models can benefit from Noisy Student even without iterative training. 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. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). 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. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. You signed in with another tab or window. 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. ImageNet images and use it as a teacher to generate pseudo labels on 300M First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset.
Self-Training With Noisy Student Improves ImageNet Classification 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. With Noisy Student, the model correctly predicts dragonfly for the image. sign in 3.5B weakly labeled Instagram images.
On . In our experiments, we use dropout[63], stochastic depth[29], data augmentation[14] to noise the student. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. 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. We sample 1.3M images in confidence intervals. Z. Yalniz, H. Jegou, K. Chen, M. Paluri, and D. Mahajan, Billion-scale semi-supervised learning for image classification, Z. Yang, W. W. Cohen, and R. Salakhutdinov, Revisiting semi-supervised learning with graph embeddings, Z. Yang, J. Hu, R. Salakhutdinov, and W. W. Cohen, Semi-supervised qa with generative domain-adaptive nets, Unsupervised word sense disambiguation rivaling supervised methods, 33rd annual meeting of the association for computational linguistics, R. Zhai, T. Cai, D. He, C. Dan, K. He, J. Hopcroft, and L. Wang, Adversarially robust generalization just requires more unlabeled data, X. Zhai, A. Oliver, A. Kolesnikov, and L. Beyer, Proceedings of the IEEE international conference on computer vision, Making convolutional networks shift-invariant again, X. Zhang, Z. Li, C. Change Loy, and D. Lin, Polynet: a pursuit of structural diversity in very deep networks, X. Zhu, Z. Ghahramani, and J. D. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, Proceedings of the 20th International conference on Machine learning (ICML-03), Semi-supervised learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, Learning transferable architectures for scalable image recognition, Architecture specifications for EfficientNet used in the paper. Figure 1(a) shows example images from ImageNet-A and the predictions of our models.
Self-Training : Noisy Student : If nothing happens, download GitHub Desktop and try again. (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. 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. Infer labels on a much larger unlabeled dataset. One might argue that the improvements from using noise can be resulted from preventing overfitting the pseudo labels on the unlabeled images. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet In Noisy Student, we combine these two steps into one because it simplifies the algorithm and leads to better performance in our preliminary experiments. Soft pseudo labels lead to better performance for low confidence data. 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. 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. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant.
During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 3429-3440. . unlabeled images. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. We duplicate images in classes where there are not enough images. 10687-10698). Due to duplications, there are only 81M unique images among these 130M images.
2023.3.1_2 - to use Codespaces.
Self-Training With Noisy Student Improves ImageNet Classification The performance drops when we further reduce it. Our main results are shown in Table1. On robustness test sets, it improves ImageNet-A top . This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. Med. In particular, we first perform normal training with a smaller resolution for 350 epochs. We used the version from [47], which filtered the validation set of ImageNet. 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. First, a teacher model is trained in a supervised fashion. 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. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . Please refer to [24] for details about mFR and AlexNets flip probability. 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. Finally, frameworks in semi-supervised learning also include graph-based methods [84, 73, 77, 33], methods that make use of latent variables as target variables [32, 42, 78] and methods based on low-density separation[21, 58, 15], which might provide complementary benefits to our method. 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 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. We use our best model Noisy Student with EfficientNet-L2 to teach student models with sizes ranging from EfficientNet-B0 to EfficientNet-B7. Noisy Students performance improves with more unlabeled data. 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. Self-training EfficientNet with Noisy Student produces correct top-1 predictions (shown in. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. This paper standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications, and proposes a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. The main use case of knowledge distillation is model compression by making the student model smaller. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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. If you get a better model, you can use the model to predict pseudo-labels on the filtered data. 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. 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. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. Apart from self-training, another important line of work in semi-supervised learning[9, 85] is based on consistency training[6, 4, 53, 36, 70, 45, 41, 51, 10, 12, 49, 2, 38, 72, 74, 5, 81]. You can also use the colab script noisystudent_svhn.ipynb to try the method on free Colab GPUs. We determine number of training steps and the learning rate schedule by the batch size for labeled images. For each class, we select at most 130K images that have the highest confidence. Edit social preview. putting back the student as the teacher. and surprising gains on robustness and adversarial benchmarks. Addressing the lack of robustness has become an important research direction in machine learning and computer vision in recent years. Abdominal organ segmentation is very important for clinical applications. Zoph et al. We iterate this process by mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model 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. The model with Noisy Student can successfully predict the correct labels of these highly difficult images.
Self-Training With Noisy Student Improves ImageNet Classification 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. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. 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. w Summary of key results compared to previous state-of-the-art models. We start with the 130M unlabeled images and gradually reduce the number of images. Self-training 1 2Self-training 3 4n What is Noisy Student? 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.