By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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%. 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. A tag already exists with the provided branch name. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. 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. 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. We also list EfficientNet-B7 as a reference. We present a simple self-training method that achieves 87.4 Infer labels on a much larger unlabeled dataset. 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. 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. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. 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. In contrast, the predictions of the model with Noisy Student remain quite stable. and surprising gains on robustness and adversarial benchmarks. Figure 1(a) shows example images from ImageNet-A and the predictions of our models. 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. 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. Self-training 1 2Self-training 3 4n What is Noisy Student? EfficientNet-L1 approximately doubles the training time of EfficientNet-L0. 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. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and In all previous experiments, the students capacity is as large as or larger than the capacity of the teacher model. Scripts used for our ImageNet experiments: Similar scripts to run predictions on unlabeled data, filter and balance data and train using the filtered data. On robustness test sets, it improves ImageNet-A top . Are you sure you want to create this branch? But training robust supervised learning models is requires this step. On . Their noise model is video specific and not relevant for image classification. Our work is based on self-training (e.g.,[59, 79, 56]). All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. 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. The abundance of data on the internet is vast. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. 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. We iterate this process by putting back the student as the teacher. The performance consistently drops with noise function removed. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. on ImageNet ReaL. 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. Self-training with Noisy Student improves ImageNet classification. We train our model using the self-training framework[59] which has three main steps: 1) train a teacher model on labeled images, 2) use the teacher to generate pseudo labels on unlabeled images, and 3) train a student model on the combination of labeled images and pseudo labeled 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. 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. 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. Zoph et al. Self-training with Noisy Student improves ImageNet classification Abstract. 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 sample 1.3M images in confidence intervals. A tag already exists with the provided branch name. We hypothesize that the improvement can be attributed to SGD, which introduces stochasticity into the training process. As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. unlabeled images. 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. Self-training with Noisy Student improves ImageNet classification. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Self-training with Noisy Student improves ImageNet classification. [57] used self-training for domain adaptation. sign in These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. 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. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). student is forced to learn harder from the pseudo labels. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. Test images on ImageNet-P underwent different scales of perturbations. . 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. Agreement NNX16AC86A, Is ADS down? The algorithm is basically self-training, a method in semi-supervised learning (. Train a larger classifier on the combined set, adding noise (noisy student). Chowdhury et al. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Self-training with Noisy Student. We iterate this process by putting back the student as the teacher. 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. 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. 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. We then select images that have confidence of the label higher than 0.3. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. Le, and J. Shlens, Using videos to evaluate image model robustness, Deep residual learning for image recognition, Benchmarking neural network robustness to common corruptions and perturbations, D. Hendrycks, K. Zhao, S. Basart, J. Steinhardt, and D. Song, Distilling the knowledge in a neural network, G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, G. Huang, Y. Work fast with our official CLI. 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. As we use soft targets, our work is also related to methods in Knowledge Distillation[7, 3, 26, 16]. Self-Training With Noisy Student Improves ImageNet Classification. 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. Noise Self-training with Noisy Student 1. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 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. 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 Flip probability is the probability that the model changes top-1 prediction for different perturbations. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . During this process, we kept increasing the size of the student model to improve the performance.