We present a simple self-training method that achieves 87.4 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. 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%. 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. 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. 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. We obtain unlabeled images from the JFT dataset [26, 11], which has around 300M images. 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). 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. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Train a classifier on labeled data (teacher). Are you sure you want to create this branch? It can be seen that masks are useful in improving classification performance. The most interesting image is shown on the right of the first row. We sample 1.3M images in confidence intervals. Figure 1(b) shows images from ImageNet-C and the corresponding predictions. Please refer to [24] for details about mCE and AlexNets error rate. Then, that teacher is used to label the unlabeled data. The performance drops when we further reduce it. Callback to apply noisy student self-training (a semi-supervised learning approach) based on: Xie, Q., Luong, M. T., Hovy, E., & Le, Q. V. (2020). Code is available at https://github.com/google-research/noisystudent. Especially unlabeled images are plentiful and can be collected with ease. A tag already exists with the provided branch name. 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. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Here we show an implementation of Noisy Student Training on SVHN, which boosts the performance of a 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. Noisy Student Training is based on the self-training framework and trained with 4-simple steps: Train a classifier on labeled data (teacher). Noisy Students performance improves with more unlabeled data. 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. 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. 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. 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 . Do better imagenet models transfer better? Noisy Student Training seeks to improve on self-training and distillation in two ways. 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 Models are available at this https URL. On robustness test sets, it improves ImageNet-A top . For instance, on ImageNet-A, Noisy Student achieves 74.2% top-1 accuracy which is approximately 57% more accurate than the previous state-of-the-art model. To intuitively understand the significant improvements on the three robustness benchmarks, we show several images in Figure2 where the predictions of the standard model are incorrect and the predictions of the Noisy Student model are correct. Are labels required for improving adversarial robustness? Agreement NNX16AC86A, Is ADS down? The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. They did not show significant improvements in terms of robustness on ImageNet-A, C and P as we did. Use Git or checkout with SVN using the web URL. Noisy Student (B7) means to use EfficientNet-B7 for both the student and the teacher. We apply dropout to the final classification layer with a dropout rate of 0.5. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Med. There was a problem preparing your codespace, please try again. We use the standard augmentation instead of RandAugment in this experiment. The main difference between Data Distillation and our method is that we use the noise to weaken the student, which is the opposite of their approach of strengthening the teacher by ensembling. We use stochastic depth[29], dropout[63] and RandAugment[14]. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. all 12, Image Classification 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. If nothing happens, download GitHub Desktop and try again. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. combination of labeled and pseudo labeled images. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. Work fast with our official CLI. 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. We use EfficientNets[69] as our baseline models because they provide better capacity for more data. 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. Then we finetune the model with a larger resolution for 1.5 epochs on unaugmented labeled images. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. With Noisy Student, the model correctly predicts dragonfly for the image. Copyright and all rights therein are retained by authors or by other copyright holders. In this section, we study the importance of noise and the effect of several noise methods used in our model. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. Noisy StudentImageNetEfficientNet-L2state-of-the-art. Our procedure went as follows. Test images on ImageNet-P underwent different scales of perturbations. These CVPR 2020 papers are the Open Access versions, provided by the. For classes where we have too many images, we take the images with the highest confidence. 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. In our experiments, we also further scale up EfficientNet-B7 and obtain EfficientNet-L0, L1 and L2. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. The Wilds 2.0 update is presented, which extends 8 of the 10 datasets in the Wilds benchmark of distribution shifts to include curated unlabeled data that would be realistically obtainable in deployment, and systematically benchmark state-of-the-art methods that leverage unlabeling data, including domain-invariant, self-training, and self-supervised methods. We then perform data filtering and balancing on this corpus. On robustness test sets, it improves For this purpose, we use the recently developed EfficientNet architectures[69] because they have a larger capacity than ResNet architectures[23]. We find that Noisy Student is better with an additional trick: data balancing. The architectures for the student and teacher models can be the same or different. First, a teacher model is trained in a supervised fashion. The biggest gain is observed on ImageNet-A: our method achieves 3.5x higher accuracy on ImageNet-A, going from 16.6% of the previous state-of-the-art to 74.2% top-1 accuracy. 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. (using extra training data). 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . If you get a better model, you can use the model to predict pseudo-labels on the filtered data. Yalniz et al. 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. sign in over the JFT dataset to predict a label for each image. . 10687-10698). Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. 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. "Self-training with Noisy Student improves ImageNet classification" pytorch implementation. It has three main steps: train a teacher model on labeled images use the teacher to generate pseudo labels on unlabeled images 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. [57] used self-training for domain adaptation. Noisy Student Training is a semi-supervised learning approach. Our study shows that using unlabeled data improves accuracy and general robustness. We determine number of training steps and the learning rate schedule by the batch size for labeled images. 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 You signed in with another tab or window. Then by using the improved B7 model as the teacher, we trained an EfficientNet-L0 student model. 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. Please This model investigates a new method. 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. A tag already exists with the provided branch name. Abdominal organ segmentation is very important for clinical applications. Self-Training with Noisy Student Improves ImageNet Classification First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. We use a resolution of 800x800 in this experiment. 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. 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. Afterward, we further increased the student model size to EfficientNet-L2, with the EfficientNet-L1 as the teacher. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. We iterate this process by putting back the student as the teacher. Self-training with Noisy Student improves ImageNet classification. Edit social preview. Learn more. corruption error from 45.7 to 31.2, and reduces ImageNet-P mean flip rate from To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. 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 . In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. Their main goal is to find a small and fast model for deployment. We use the same architecture for the teacher and the student and do not perform iterative training. 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. Different types of. EfficientNet-L1 approximately doubles the training time of EfficientNet-L0.
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