self training with noisy student improves imagenet classification

https://arxiv.org/abs/1911.04252. ImageNet . We iterate this process by putting back the student as the teacher. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. Self-training with Noisy Student improves ImageNet classification. 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 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. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. 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. 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. To noise the student, we use dropout[63], data augmentation[14] and stochastic depth[29] during its training. 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. First, a teacher model is trained in a supervised fashion. The algorithm is basically self-training, a method in semi-supervised learning (. The pseudo labels can be soft (a continuous distribution) or hard (a one-hot distribution). Here we study how to effectively use out-of-domain data. Lastly, we will show the results of benchmarking our model on robustness datasets such as ImageNet-A, C and P and adversarial robustness. We then select images that have confidence of the label higher than 0.3. Overall, EfficientNets with Noisy Student provide a much better tradeoff between model size and accuracy when compared with prior works. Prior works on weakly-supervised learning require billions of weakly labeled data to improve state-of-the-art ImageNet models. However, the additional hyperparameters introduced by the ramping up schedule and the entropy minimization make them more difficult to use at scale. Hence we use soft pseudo labels for our experiments unless otherwise specified. Soft pseudo labels lead to better performance for low confidence data. On . 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. Lastly, we apply the recently proposed technique to fix train-test resolution discrepancy[71] for EfficientNet-L0, L1 and L2. Iterative training is not used here for simplicity. ImageNet-A test set[25] consists of difficult images that cause significant drops in accuracy to state-of-the-art models. 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]). In other words, small changes in the input image can cause large changes to the predictions. As shown in Table3,4 and5, when compared with the previous state-of-the-art model ResNeXt-101 WSL[44, 48] trained on 3.5B weakly labeled images, Noisy Student yields substantial gains on robustness datasets. A common workaround is to use entropy minimization or ramp up the consistency loss. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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Train a classifier on labeled data (teacher). Please refer to [24] for details about mFR and AlexNets flip probability. In the following, we will first describe experiment details to achieve our results. We then train a larger EfficientNet as a student model on the combination of labeled and pseudo labeled images. Ranked #14 on 1ImageNetTeacher NetworkStudent Network 2T [JFT dataset] 3 [JFT dataset]ImageNetStudent Network 4Student Network1DropOut21 1S-TTSS equal-or-larger student model Please refer to [24] for details about mCE and AlexNets error rate. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. 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. Self-Training With Noisy Student Improves ImageNet Classification Abstract: 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. Our model is also approximately twice as small in the number of parameters compared to FixRes ResNeXt-101 WSL. 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. Self-training You signed in with another tab or window. 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. . As a comparison, our method only requires 300M unlabeled images, which is perhaps more easy to collect. 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). 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. We start with the 130M unlabeled images and gradually reduce the number of images. Hence, EfficientNet-L0 has around the same training speed with EfficientNet-B7 but more parameters that give it a larger capacity. For example, with all noise removed, the accuracy drops from 84.9% to 84.3% in the case with 130M unlabeled images and drops from 83.9% to 83.2% in the case with 1.3M unlabeled images. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. The architecture specifications of EfficientNet-L0, L1 and L2 are listed in Table 7. We determine number of training steps and the learning rate schedule by the batch size for labeled images. 10687-10698 Abstract We used the version from [47], which filtered the validation set of ImageNet. IEEE Transactions on Pattern Analysis and Machine Intelligence. If nothing happens, download Xcode and try again. [50] used knowledge distillation on unlabeled data to teach a small student model for speech recognition. 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. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative task. Self-training with noisy student improves imagenet classification, in: Proceedings of the IEEE/CVF Conference on Computer . 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. In this section, we study the importance of noise and the effect of several noise methods used in our 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. Use Git or checkout with SVN using the web URL. . We apply RandAugment to all EfficientNet baselines, leading to more competitive baselines. To achieve this result, we first train an EfficientNet model on labeled Hence, whether soft pseudo labels or hard pseudo labels work better might need to be determined on a case-by-case basis. 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. We use the labeled images to train a teacher model using the standard cross entropy loss. 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. Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le. 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. Are you sure you want to create this branch? 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. We then perform data filtering and balancing on this corpus. putting back the student as the teacher. 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. When data augmentation noise is used, the student must ensure that a translated image, for example, should have the same category with a non-translated image. We investigate the importance of noising in two scenarios with different amounts of unlabeled data and different teacher model accuracies. 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. combination of labeled and pseudo labeled images. 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. . Self-Training Noisy Student " " Self-Training . sign in 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. supervised model from 97.9% accuracy to 98.6% accuracy. Chowdhury et al. It implements SemiSupervised Learning with Noise to create an Image Classification. 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. Please on ImageNet ReaL. Finally, the training time of EfficientNet-L2 is around 2.72 times the training time of EfficientNet-L1. Also related to our work is Data Distillation[52], which ensembled predictions for an image with different transformations to teach a student network. We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. By clicking accept or continuing to use the site, you agree to the terms outlined in our. 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. Infer labels on a much larger unlabeled dataset. In both cases, we gradually remove augmentation, stochastic depth and dropout for unlabeled images, while keeping them for labeled images. We use EfficientNet-B4 as both the teacher and the student. During the generation of the pseudo labels, the teacher is not noised so that the pseudo labels are as accurate as possible. Noisy Student Training seeks to improve on self-training and distillation in two ways. Image Classification We vary the model size from EfficientNet-B0 to EfficientNet-B7[69] and use the same model as both the teacher and the student. As stated earlier, we hypothesize that noising the student is needed so that it does not merely learn 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. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. The method, named self-training with Noisy Student, also benefits from the large capacity of EfficientNet family. on ImageNet ReaL Le. This model investigates a new method. The most interesting image is shown on the right of the first row. If nothing happens, download GitHub Desktop and try again. 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. 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. Use Git or checkout with SVN using the web URL. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. Use a model to predict pseudo-labels on the filtered data: This is not an officially supported Google product. We found that self-training is a simple and effective algorithm to leverage unlabeled data at scale. Algorithm1 gives an overview of self-training with Noisy Student (or Noisy Student in short). 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. 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. Noisy Students performance improves with more unlabeled data. 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. 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 shown in Figure 3, Noisy Student leads to approximately 10% improvement in accuracy even though the model is not optimized for adversarial robustness. Noisy Student Training is a semi-supervised learning approach. 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. The architectures for the student and teacher models can be the same or different. 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. In addition to improving state-of-the-art results, we conduct additional experiments to verify if Noisy Student can benefit other EfficienetNet models. Different types of. Copyright and all rights therein are retained by authors or by other copyright holders. The baseline model achieves an accuracy of 83.2. Our largest model, EfficientNet-L2, needs to be trained for 3.5 days on a Cloud TPU v3 Pod, which has 2048 cores. Finally, we iterate the process by putting back the student as a teacher to generate new pseudo labels and train a new student. This article demonstrates the first tool based on a convolutional Unet++ encoderdecoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. 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. It can be seen that masks are useful in improving classification performance. 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. Hence the total number of images that we use for training a student model is 130M (with some duplicated images). ImageNet-A top-1 accuracy from 16.6 mCE (mean corruption error) is the weighted average of error rate on different corruptions, with AlexNets error rate as a baseline. Conclusion, Abstract , ImageNet , web-scale extra labeled images weakly labeled Instagram images weakly-supervised learning . Self-training was previously used to improve ResNet-50 from 76.4% to 81.2% top-1 accuracy[76] which is still far from the state-of-the-art accuracy. to use Codespaces. Our main results are shown in Table1. A tag already exists with the provided branch name. Most existing distance metric learning approaches use fully labeled data Self-training achieves enormous success in various semi-supervised and

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