Code for Noisy Student Training. Especially unlabeled images are plentiful and can be collected with ease. However, manually annotating organs from CT scans is time . We evaluate our EfficientNet-L2 models with and without Noisy Student against an FGSM attack. Our finding is consistent with similar arguments that using unlabeled data can improve adversarial robustness[8, 64, 46, 80]. A common workaround is to use entropy minimization or ramp up the consistency loss. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. 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. We present a simple self-training method that achieves 87.4 Chowdhury et al. 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 . This material is presented to ensure timely dissemination of scholarly and technical work. 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. task. This way, the pseudo labels are as good as possible, and the noised student is forced to learn harder from the pseudo labels. Test images on ImageNet-P underwent different scales of perturbations. We then select images that have confidence of the label higher than 0.3. (or is it just me), Smithsonian Privacy 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. 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). sign in We iterate this process by putting back the student as the teacher. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. The performance drops when we further reduce it. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. On . 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. 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. Our study shows that using unlabeled data improves accuracy and general robustness. to use Codespaces. Similar to[71], we fix the shallow layers during finetuning. CLIP (Contrastive Language-Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.The idea of zero-data learning dates back over a decade [^reference-8] but until recently was mostly studied in computer vision as a way of generalizing to unseen object categories. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. We find that Noisy Student is better with an additional trick: data balancing. 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. Noisy Student Training is a semi-supervised training method which achieves 88.4% top-1 accuracy on ImageNet We call the method self-training with Noisy Student to emphasize the role that noise plays in the method and results. For RandAugment, we apply two random operations with the magnitude set to 27. Their noise model is video specific and not relevant for image classification. Our experiments showed that our model significantly improves accuracy on ImageNet-A, C and P without the need for deliberate data augmentation. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. 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. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. 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. We use the same architecture for the teacher and the student and do not perform iterative training. First, it makes the student larger than, or at least equal to, the teacher so the student can better learn from a larger dataset. Please refer to [24] for details about mFR and AlexNets flip probability. 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. https://arxiv.org/abs/1911.04252. We find that using a batch size of 512, 1024, and 2048 leads to the same performance. Our work is based on self-training (e.g.,[59, 79, 56]). Lastly, we follow the idea of compound scaling[69] and scale all dimensions to obtain EfficientNet-L2. The baseline model achieves an accuracy of 83.2. supervised model from 97.9% accuracy to 98.6% accuracy. Train a classifier on labeled data (teacher). 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-mentoring outperforms data augmentation and self training. possible. 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 nothing happens, download Xcode and try again. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. First, a teacher model is trained in a supervised fashion. 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. In typical self-training with the teacher-student framework, noise injection to the student is not used by default, or the role of noise is not fully understood or justified. It 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 shows that it is helpful to train a large model with high accuracy using Noisy Student when small models are needed for deployment. 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. Specifically, as all classes in ImageNet have a similar number of labeled images, we also need to balance the number of unlabeled images for each class. 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. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . Noisy Student improves adversarial robustness against an FGSM attack though the model is not optimized for adversarial robustness. Works based on pseudo label[37, 31, 60, 1] are similar to self-training, but also suffers the same problem with consistency training, since it relies on a model being trained instead of a converged model with high accuracy to generate pseudo labels. 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. 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. 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. Learn more. The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. In other words, using Noisy Student makes a much larger impact to the accuracy than changing the architecture. , have shown that computer vision models lack robustness. Secondly, to enable the student to learn a more powerful model, we also make the student model larger than the teacher model. 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. For this purpose, we use a much larger corpus of unlabeled images, where some images may not belong to any category in ImageNet. . 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. On robustness test sets, it improves 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. ImageNet images and use it as a teacher to generate pseudo labels on 300M It is expensive and must be done with great care. Unlike previous studies in semi-supervised learning that use in-domain unlabeled data (e.g, ., CIFAR-10 images as unlabeled data for a small CIFAR-10 training set), to improve ImageNet, we must use out-of-domain unlabeled data. 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. 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 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. Stochastic Depth is a simple yet ingenious idea to add noise to the model by bypassing the transformations through skip connections. 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. Flip probability is the probability that the model changes top-1 prediction for different perturbations. Notably, EfficientNet-B7 achieves an accuracy of 86.8%, which is 1.8% better than the supervised model. Parthasarathi et al. You signed in with another tab or window. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 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. We also study the effects of using different amounts of unlabeled data. We sample 1.3M images in confidence intervals. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. This work adopts the noisy-student learning method, and adopts 3D nnUNet as the segmentation model during the experiments, since No new U-Net is the state-of-the-art medical image segmentation method and designs task-specific pipelines for different tasks. 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. 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]. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. (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 apply dropout to the final classification layer with a dropout rate of 0.5. If nothing happens, download GitHub Desktop and try again. Lastly, we trained another EfficientNet-L2 student by using the EfficientNet-L2 model as the teacher. If nothing happens, download Xcode and try again. 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. Self-training with Noisy Student improves ImageNet classification. 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). w Summary of key results compared to previous state-of-the-art models. Self-training with Noisy Student improves ImageNet classification. 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. We use EfficientNet-B0 as both the teacher model and the student model and compare using Noisy Student with soft pseudo labels and hard pseudo labels. 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. over the JFT dataset to predict a label for each image. 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. Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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%. 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model 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. 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. 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. 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. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Please refer to [24] for details about mCE and AlexNets error rate. However state-of-the-art vision models are still trained with supervised learning which requires a large corpus of labeled images to work well. During this process, we kept increasing the size of the student model to improve the performance. We do not tune these hyperparameters extensively since our method is highly robust to them. We iterate this process by putting back the student as the teacher. Are you sure you want to create this branch? 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. These works constrain model predictions to be invariant to noise injected to the input, hidden states or model parameters. Self-training 1 2Self-training 3 4n What is Noisy Student? 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 ImageNet, which is 1.0 (2) With out-of-domain unlabeled images, hard pseudo labels can hurt the performance while soft pseudo labels leads to robust performance. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. This attack performs one gradient descent step on the input image[20] with the update on each pixel set to . Models are available at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet. Amongst other components, Noisy Student implements Self-Training in the context of Semi-Supervised Learning. 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. The width. Next, a larger student model is trained on the combination of all data and achieves better performance than the teacher by itself.OUTLINE:0:00 - Intro \u0026 Overview1:05 - Semi-Supervised \u0026 Transfer Learning5:45 - Self-Training \u0026 Knowledge Distillation10:00 - Noisy Student Algorithm Overview20:20 - Noise Methods22:30 - Dataset Balancing25:20 - Results30:15 - Perturbation Robustness34:35 - Ablation Studies39:30 - Conclusion \u0026 CommentsPaper: https://arxiv.org/abs/1911.04252Code: https://github.com/google-research/noisystudentModels: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnetAbstract: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 Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn the teachers knowledge. The total gain of 2.4% comes from two sources: by making the model larger (+0.5%) and by Noisy Student (+1.9%). As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. Self-training with Noisy Student. 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. This accuracy is 1.0% better than the previous state-of-the-art ImageNet accuracy which requires 3.5B weakly labeled Instagram images. 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. On, International journal of molecular sciences. Noisy Student (B7) means to use EfficientNet-B7 for both the student and 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. 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. As shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. For instance, on the right column, as the image of the car undergone a small rotation, the standard model changes its prediction from racing car to car wheel to fire engine.