We iterate this process by putting back the student as the teacher. The architectures for the student and teacher models can be the same or different. to use Codespaces. . Do better imagenet models transfer better? to noise the student. Noise Self-training with Noisy Student 1. labels, the teacher is not noised so that the pseudo labels are as good as Papers With Code is a free resource with all data licensed under. 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. By clicking accept or continuing to use the site, you agree to the terms outlined in our. There was a problem preparing your codespace, please try again. Specifically, we train the student model for 350 epochs for models larger than EfficientNet-B4, including EfficientNet-L0, L1 and L2 and train the student model for 700 epochs for smaller models. This is an important difference between our work and prior works on teacher-student framework whose main goal is model compression. 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. Noisy Student (EfficientNet) - huggingface.co 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. 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 performance drops when we further reduce it. Edit social preview. [76] also proposed to first only train on unlabeled images and then finetune their model on labeled images as the final stage. For RandAugment, we apply two random operations with the magnitude set to 27. Noisy Student Training is based on the self-training framework and trained with 4 simple steps: Train a classifier on labeled data (teacher). 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]. 10687-10698). Noisy Student Training seeks to improve on self-training and distillation in two ways. 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. Self-training with Noisy Student - Astrophysical Observatory. 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. Then, that teacher is used to label the unlabeled data. 3429-3440. . The main difference between our work and these works is that they directly optimize adversarial robustness on unlabeled data, whereas we show that self-training with Noisy Student improves robustness greatly even without directly optimizing robustness. Self-Training for Natural Language Understanding! Self-Training : Noisy Student : FixMatch-LS: Semi-supervised skin lesion classification with label Especially unlabeled images are plentiful and can be collected with ease. The swing in the picture is barely recognizable by human while the Noisy Student model still makes the correct prediction. 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. On robustness test sets, it improves It implements SemiSupervised Learning with Noise to create an Image Classification. The top-1 accuracy reported in this paper is the average accuracy for all images included in ImageNet-P. 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. The learning rate starts at 0.128 for labeled batch size 2048 and decays by 0.97 every 2.4 epochs if trained for 350 epochs or every 4.8 epochs if trained for 700 epochs. Work fast with our official CLI. 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. To achieve this result, we first train an EfficientNet model on labeled 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. Instructions on running prediction on unlabeled data, filtering and balancing data and training using the stored predictions. Stochastic depth is proposed, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time and reduces training time substantially and improves the test error significantly on almost all data sets that were used for evaluation. 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. This way, we can isolate the influence of noising on unlabeled images from the influence of preventing overfitting for labeled images. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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 1 2Self-training 3 4n What is Noisy Student? Similar to[71], we fix the shallow layers during finetuning. 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. Efficient Nets with Noisy Student Training | by Bharatdhyani | Towards We then train a larger EfficientNet as a student model on the combination of labeled and pseudo 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%). The baseline model achieves an accuracy of 83.2. Code is available at this https URL.Authors: Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. 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The algorithm is iterated a few times by treating the student as a teacher to relabel the unlabeled data and training a new student. An important contribution of our work was to show that Noisy Student can potentially help addressing the lack of robustness in computer vision models. 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 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. Self-Training With Noisy Student Improves ImageNet Classification on ImageNet, which is 1.0 . We then perform data filtering and balancing on this corpus. possible. We use EfficientNet-B4 as both the teacher and the student. 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. 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-mentoring: : A new deep learning pipeline to train a self Figure 1(c) shows images from ImageNet-P and the corresponding predictions. We also list EfficientNet-B7 as a reference. The score is normalized by AlexNets error rate so that corruptions with different difficulties lead to scores of a similar scale. 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. The top-1 accuracy of prior methods are computed from their reported corruption error on each corruption. 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. Using Noisy Student (EfficientNet-L2) as the teacher leads to another 0.8% improvement on top of the improved results. 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.